Patient biomarker monitoring with outcomes to monitor overall healthcare delivery

ABSTRACT

Examples herein may include a computer-implemented method for providing outcome tracking of a plurality of patients, which may include generating a respective expected patient biomarker dataset for each of the plurality of patients, wherein the expected patient biomarker dataset may represent the expected values of a patient biomarker over the duration of the patient&#39;s recovery. The computer implementing method may include receiving respective actual patient biomarker data from respective patient sensor systems for each of the plurality of patients. The method may include aggregating the respective expected patient biomarker dataset and the respective actual patient biomarker data for each of the plurality of patients. The method may include determining differences between the respective expected patient biomarker data and the respective actual patient biomarker data. The method may include generating a treatment notification based on the differences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following, filed contemporaneously,the contents of each of which are incorporated by reference herein:

-   -   Attorney Docket No. END9290USNP1, titled METHOD OF ADJUSTING A        SURGICAL PARAMETER BASED ON BIOMARKER MEASUREMENTS.

BACKGROUND

The surgical environment and related surgical systems may incorporatesensor systems. And sensor systems may be used to measure (among otherthings) various biomarkers.

Measuring biomarkers can be useful in healthcare. Such measurements maybe used to indicate various aspects regarding the health of patients.And such measurements may be helpful when made during surgery, as wellas, before and/or after surgery.

The integration of data from such measurements may be used to improvethe overall performance of health care delivery, leading to improvedpatient outcomes.

SUMMARY

Examples herein may include a computer system for outcome tracking of aplurality of patients, which may include a processor and a memorycoupled to the processor. The memory may store instructions, that whenexecuted by the processor, may cause the computer system to generate arespective expected patient biomarker dataset for each of the pluralityof patients, wherein the expected patient biomarker dataset mayrepresent the expected values of a patient biomarker over the durationof the patient's recovery. The computer system may receive respectiveactual patient biomarker data from respective patient sensor systems foreach of the plurality of patients. The computer system may determinedifferences between the respective expected patient biomarker data andthe respective actual patient biomarker data for each of the pluralityof patients. The computer system may aggregate the differences. Thecomputer system may generate a treatment notification based on theaggregation of the differences.

Examples herein may include a computer-implemented method for providingoutcome tracking of a plurality patients, which may include generating arespective expected patient biomarker dataset for each of the pluralityof patients, wherein the expected patient biomarker dataset mayrepresent the expected values of a patient biomarker over the durationof the patient's recovery. The computer implementing method may includereceiving respective actual patient biomarker data from respectivepatient sensor systems for each of the plurality of patients. The methodmay include aggregating the respective expected patient biomarkerdataset and the respective actual patient biomarker data for each of theplurality of patients. The method may include determining differencesbetween the respective expected patient biomarker data and therespective actual patient biomarker data. The method may includegenerating a treatment notification based on the differences.

Examples herein may include a facility analytics system for outcometracking of a plurality of patients, which may include a processor and amemory coupled to the processor. The memory may store instructions, thatwhen executed by the processor, may cause the facility analytics systemto establish communication with a computing device. They facilityanalytics system may receive a treatment notification from the computingdevice, wherein the treatment notification may be based on differencesof aggregated respective expected patient biomarker data and aggregatedrespective actual patient biomarker data for a plurality of patients.The facility analytics system may perform facility analytics based onthe treatment notification.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of a computer-implemented patient and surgeonmonitoring system.

FIG. 1B is a block diagram of an example relationship among sensingsystems, biomarkers, and physiologic systems.

FIG. 2A shows an example of a surgeon monitoring system in a surgicaloperating room.

FIG. 2B shows an example of a patient monitoring system (e.g., acontrolled patient monitoring system).

FIG. 2C shows an example of a patient monitoring system (e.g., anuncontrolled patient monitoring system).

FIG. 3 illustrates an example surgical hub paired with various systems.

FIG. 4 illustrates a surgical data network having a set of communicationsurgical hubs configured to connect with a set of sensing systems, anenvironmental sensing system, a set of devices, etc.

FIG. 5 illustrates an example computer-implemented interactive surgicalsystem that may be part of a surgeon monitoring system.

FIG. 6A illustrates a surgical hub comprising a plurality of modulescoupled to a modular control tower.

FIG. 6B illustrates an example of a controlled patient monitoringsystem.

FIG. 6C illustrates an example of an uncontrolled patient monitoringsystem.

FIG. 7A illustrates a logic diagram of a control system of a surgicalinstrument or a tool.

FIG. 7B shows an exemplary sensing system with a sensor unit and a dataprocessing and communication unit.

FIG. 7C shows an exemplary sensing system with a sensor unit and a dataprocessing and communication unit.

FIG. 7D shows an exemplary sensing system with a sensor unit and a dataprocessing and communication unit.

FIG. 8 illustrates an exemplary timeline of art illustrative surgicalprocedure indicating adjusting operational parameters of a surgicaldevice based on a surgeon biomarker level.

FIG. 9 is a block diagram of the computer-implemented interactivesurgeon/patient monitoring system.

FIG. 10 shows an example surgical system that includes a handle having acontroller and a motor, an adapter releasably coupled to the handle, anda loading unit releasably coupled to the adapter.

FIGS. 11A-11D illustrate examples of sensing systems that may be usedfor monitoring surgeon biomarkers or patient biomarkers.

FIG. 12 is a block diagram of a patient monitoring system or a surgeonmonitoring system.

FIG. 13 shows an example of a computer implemented patient and surgeonmonitoring system that aggregates biomarker data.

FIGS. 14A-14C show an example of a computer-implemented patient andsurgeon monitoring system monitoring heart rate data of a group ofpatients.

FIGS. 15A-15C show another example of the computer-implemented patientand surgeon monitoring system monitoring heart rate data of a group ofpatients.

FIG. 16 shows example facility analytics system that can be viewed on acomputing device by an HCP.

FIG. 17 illustrates a process for a computing-implemented patient andsurgeon monitoring system that aggregates biomarker data.

FIG. 18 illustrates a process for a facility analytics system foranalyzing patient biomarker data.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a computer-implemented patient and surgeonmonitoring system 20000. The patient and surgeon monitoring system 20000may include one or more surgeon monitoring systems 20002 and a one ormore patient monitoring systems (e.g., one or more controlled patientmonitoring systems 20003 and one or more uncontrolled patient monitoringsystems 20004). Each surgeon monitoring system 20002 may include acomputer-implemented interactive surgical system. Each surgeonmonitoring system 20002 may include at least one of the following: asurgical hub 20006 in communication with a cloud computing system 20008,for example, as described in FIG. 2A. Each of the patient monitoringsystems may include at least one of the following: a surgical hub 20006or a computing device 20016 in communication with a cloud computingsystem 20008, for example, as further described in FIG. 2B and FIG. 2C.The cloud computing system 20008 may include at least one remote cloudserver 20009 and at least one remote cloud storage unit 20010. Each ofthe surgeon monitoring systems 20002, the controlled patient monitoringsystems 20003, or the uncontrolled patient monitoring systems 20004 mayinclude a wearable sensing system 20011, an environmental sensing system20015, a robotic system 20013, one or more intelligent instruments20014, human interface system 20012, etc. The human interface system isalso referred herein as the human interface device. The wearable sensingsystem 20011 may include one or more surgeon sensing systems, and/or oneor more patient sensing systems. The environmental sensing system 20015may include one or more devices, for example, used for measuring one ormore environmental attributes, for example, as further described in FIG.2A. The robotic system 20013 (same as 20034 in FIG. 2A) may include aplurality of devices used for performing a surgical procedure, forexample, as further described in FIG. 2A.

A surgical hub 20006 may have, cooperative interactions with one of moremeans of displaying the image from the laparoscopic scope andinformation from one or more other smart devices and one or more sensingsystems 20011. The surgical hub 20006 may interact with one or moresensing systems 20011, one or more smart devices, and multiple displays.The surgical hub 20006 may be configured to gather measurement data fromthe one or more sensing systems 20011 and send notifications or controlmessages to the one or more sensing systems 20011. The surgical hub20006 may send and/or receive information including notificationinformation to and/or from the human interface system 20012. The humaninterface system 20012 may include one or more human interface devices(HIDs). The surgical hub 20006 may send and/or receive notificationinformation or control information to audio, display and/or controlinformation to various devices that are in communication with thesurgical hub.

FIG. 1B is a block diagram of an example relationship among sensingsystems 20001, biomarkers 20005, and physiologic systems 20007. Therelationship may be employed in the computer-implemented patient andsurgeon monitoring system 20000 and in the systems, devices, and methodsdisclosed herein. For example, the sensing systems 20001 may include thewearable sensing system 20011 (which may include one or more surgeonsensing systems and one or more patient sensing systems) and theenvironmental sensing system 20015 as discussed in FIG. 1A. The one ormore sensing systems 20001 may measure data relating to variousbiomarkers 20005. The one or more sensing systems 20001 may measure thebiomarkers 20005 using one or more sensors, for example, photosensors(e.g., photodiodes, photoresistors), mechanical sensors (e.g., motionsensors), acoustic sensors, electrical sensors, electrochemical sensors,thermoelectric sensors, infrared sensors, etc. The one or more sensorsmay measure the biomarkers 20005 as described herein using one of moreof the following sensing technologies: photoplethysmography,electrocardiography, electroencephalography, colorimetry, impedimentary,potentiometry, amperometry, etc.

The biomarkers 20005 measured by the one or more sensing systems 20001may include, but are not limited to, sleep, core body temperature,maximal oxygen consumption, physical activity, alcohol consumption,respiration rate, oxygen saturation, blood pressure, blood sugar, heartrate variability, blood potential of hydrogen, hydration state, heartrate, skin conductance, peripheral temperature, tissue perfusionpressure, coughing and sneezing, gastrointestinal motility,gastrointestinal tract imaging, respiratory tract bacteria, edema,mental aspects, sweat, circulating tumor cells, autonomic tone,circadian rhythm, and/or menstrual cycle.

The biomarkers 20005 may relate to physiologic systems 20007, which mayinclude, but are not limited to, behavior and psychology, cardiovascularsystem, renal system, skin system, nervous system, gastrointestinalsystem, respiratory system, endocrine system, immune system, tumor,musculoskeletal system, and/or reproductive system. Information from thebiomarkers may be determined and/or used by the computer-implementedpatient and surgeon monitoring system 20000, for example. Theinformation from the biomarkers may be determined and/or used by thecomputer-implemented patient and surgeon monitoring system 20000 toimprove said systems and/or to improve patient outcomes, for example.

The one or more sensing systems 20001, biomarkers 20005, andphysiological systems 20007 are described in more detail below.

A sleep sensing system may measure sleep data, including heart rate,respiration rate, body temperature, movement, and/or brain signals. Thesleep sensing system may measure sleep data using a photoplethysmogram(PPG), electrocardiogram (ECG), microphone, thermometer, accelerometer,electroencephalogram (EEG), and/or the like. The sleep sensing systemmay include a wearable device such as a wristband.

Based on the measured sleep data, the sleep sensing system may detectsleep biomarkers, including but not limited to, deep sleep quantifier,REM sleep quantifier, disrupted sleep quantifier, and/or sleep duration.The sleep sensing system may transmit the measured sleep data to aprocessing unit. The sleep sensing system and/or the processing unit maydetect deep sleep when the sensing system senses sleep data, includingreduced heart rate, reduced respiration rate, reduced body temperature,and/or reduced movement. The sleep sensing system may generate a sleepquality score based on the detected sleep physiology.

In an example, the sleep sensing system may send the sleep quality scoreto a computing system, such as a surgical hub. In an example, the sleepsensing system may send the detected sleep biomarkers to a computingsystem, such as a surgical hub. In an example, the sleep sensing systemmay send the measured sleep data to a computing system, such as asurgical hub. The computing system may derive sleep physiology based onthe received measured data and generate one or more sleep biomarkerssuch as deep sleep quantifiers. The computing system may generate atreatment plan, including a pain management strategy, based on the sleepbiomarkers. The surgical hub may detect potential risk factors orconditions, including systemic inflammation and/or reduced immunefunction, based on the sleep biomarkers.

A core body temperature sensing system may measure body temperature dataincluding temperature, emitted frequency spectra, and/or the like. Thecore body temperature sensing system may measure body temperature datausing some combination of thermometers and/or radio telemetry. The corebody temperature sensing system may include an ingestible thermometerthat measures the temperature of the digestive tract. The ingestiblethermometer may wirelessly transmit remeasured temperature data. Thecore body temperature sensing system may include a wearable antenna thatmeasures body emission spectra. The core body temperature sensing systemmay include a wearable patch that measures body temperature data.

The core body temperature sensing system may calculate body temperatureusing the body temperature data. The core body temperature sensingsystem may transmit the calculated body temperature to a monitoringdevice. The monitoring device may track the core body temperature dataover time and display it to a user.

The core body temperature sensing system may process the core bodytemperature data locally or send the data to a processing unit and/or acomputing system. Based on the measured temperature data, the core bodytemperature sensing system may detect body temperature-relatedbiomarkers, complications and/or contextual information that may includeabnormal temperature, characteristic fluctuations, infection, menstrualcycle, climate, physical activity, and/or sleep.

For example, the core body temperature sensing system may detectabnormal temperature based on temperature being outside the range of36.5° C. and 37.5° C. For example, the core body temperature sensingsystem may detect post-operation infection or sepsis based on certaintemperature fluctuations and/or when core body temperature reachesabnormal levels. For example, the core body temperature sensing systemmay detect physical activities using measured fluctuations in core bodytemperature.

For example, the body temperature sensing system may detect core bodytemperature data and trigger the sensing system to emit a cooling orheating element to raise or lower the body temperature in line with themeasured ambient temperature.

In an example, the body temperature sensing system may send the bodytemperature-related biomarkers to a computing system, such as a surgicalhub. In an example, the body temperature sensing system may send themeasured body temperature data to the computing system. The computersystem may derive the body temperature-related biomarkers based on thereceived body temperature data.

A maximal oxygen consumption (VO2 max) sensing system may measure VO2max data, including oxygen uptake, heart rate, and/or movement speed.The VO2 max sensing system may measure VO2 max data during physicalactivities, including, running and/or walking. The VO2 max sensingsystem may include a wearable device. The VO2 max sensing system mayprocess the VO2 max data locally or transmit the data to a processingunit and/or a computing system.

Based on the measured VO2 max data, the sensing system and/or thecomputing system may derive, detect, and/or calculate biomarkers,including a VO2 max quantifier, VO2 max score, physical activity, and/orphysical activity intensity. The VO2 max sensing system may selectcorrect VO2 max data measurements during correct time segments tocalculate accurate VO2 max information. Based on the VO2 maxinformation, the sensing system may detect dominating cardio, vascular,and/or respiratory limiting factors. Based on the VO2 max information,risks may be predicted including adverse cardiovascular events insurgery and/or increased risk of in-hospital morbidity. For example,increased risk of in-hospital morbidity may be detected when thecalculated VO2 max quantifier falls below a specific threshold, such as18.2 ml kg-1 min-1.

In an example, the VO2 max sensing system may send the VO2 max-relatedbiomarkers to a computing system, such as a surgical hub. In an example,the VO2 max sensing system may send the measured VO2 max data to thecomputing system. The computer system may derive the VO2 max-relatedbiomarkers based on the received VO2 max data.

A physical activity sensing system may measure physical activity data,including heart rate, motion, location, posture, range-of-motion,movement speed, and/or cadence. The physical activity sensing system maymeasure physical activity data including accelerometer, magnetometer,gyroscope, global positioning system (GPS), PPG, and/or ECG. Thephysical activity sensing system may include a wearable device. Thephysical activity wearable device may include, but is not limited to, awatch, wrist band, vest, glove, belt, headband, shoe, and/or garment.The physical activity sensing system may locally process the physicalactivity data or transmit the data to a processing unit and/or acomputing system.

Based on the measured physical activity data, the physical activitysensing system may detect physical activity-related biomarkers,including but not limited to exercise activity, physical activityintensity, physical activity frequency, and/or physical activityduration. The physical activity sensing system may generate physicalactivity summaries based on physical activity information.

For example, the physical activity sensing system may send physicalactivity information to a computing system. For example, the physicalactivity sensing system may send measured data to a computing system.The computing system may, based on the physical activity information,generate activity summaries, training plans, and recovery plans. Thecomputing system may store the physical activity information in userprofiles. The computing system may display the physical activityinformation graphically. The computing system may select certainphysical activity information and display the information together orseparately.

An alcohol consumption sensing system may measure alcohol consumptiondata including alcohol and/or sweat. The alcohol consumption sensingsystem may use a pump to measure perspiration. The pump may use a fuelcell that reacts with ethanol to detect alcohol presence inperspiration. The alcohol consumption sensing system may include awearable device, for example, a wristband. The alcohol consumptionsensing system may use microfluidic applications to measure alcoholand/or sweat. The microfluidic applications may measure alcoholconsumption data using sweat stimulation and wicking with commercialethanol sensors. The alcohol consumption sensing system may include awearable patch that adheres to skin. The alcohol consumption sensingsystem may include a breathalyzer. The sensing system may process thealcohol consumption data locally or transmit the data to a processingunit and/or computing system.

Based on the measured alcohol consumption data, the sensing system maycalculate a blood alcohol concentration. The sensing system may detectalcohol consumption conditions and/or risk factors. The sensing systemmay detect alcohol consumption-related biomarkers including reducedimmune capacity, cardiac insufficiency, and/or arrhythmia. Reducedimmune capacity may occur when a patient consumes three or inure alcoholunits per day. The sensing system may detect risk factors forpostoperative complications including infection, cardiopulmonarycomplication, and/or bleeding episodes. Healthcare providers may use thedetected risk factors for predicting or detecting post-operative orpost-surgical complications, for example, to affect decisions andprecautions taken during post-surgical care.

In an example, the alcohol consumption sensing system may send thealcohol consumption-related biomarkers to a computing system, such as asurgical hub. In an example, the alcohol consumption sensing system maysend the measured alcohol consumption data to the computing system. Thecomputer system may derive the alcohol consumption-related biomarkersbased on the received alcohol consumption data.

A respiration sensing system may measure respiration rate data,including inhalation, exhalation, chest cavity movement, and/or airflow.The respiration sensing system may measure respiration rate datamechanically and/or acoustically. The respiration sensing system maymeasure respiration rate data using a ventilator. The respirationsensing system may measure respiration data mechanically by detectingchest cavity movement. Two or more applied electrodes on a chest maymeasure the changing distance between the electrodes to detect chestcavity expansion and contraction during a breath. The respirationsensing system may include a wearable skin patch. The respirationsensing system may measure respiration data acoustically using amicrophone to record airflow sounds. The respiration sensing system maylocally process the respiration data or transmit the data to aprocessing unit and/or computing system.

Based on measured respiration data, the respiration sensing system maygenerate respiration-related biomarkers including breath frequency,breath pattern, and/or breath depth. Based on the respiratory rate data,the respiration sensing system may generate a respiration quality score.

Based on the respiration rate data, the respiration sensing system maydetect respiration-related biomarkers including irregular breathing,pain, air leak, collapsed lung, lung tissue and strength, and/or shock.For example, the respiration sensing system may detect irregularitiesbased on changes in breath frequency, breath pattern, and/or breathdepth. For example, the respiration sensing system may detectpost-operative pain based on short, sharp breaths. For example, therespiration sensing system may detect an air leak based on a volumedifference between inspiration and expiration. For example, therespiration sensing system may detect a collapsed lung based onincreased breath frequency combined with a constant volume inhalation.For example, the respiration sensing system may detect lung tissuestrength and shock including systemic inflammatory response syndrome(SIRS) based on an increase in respiratory rate, including more than 2standard deviations. In an example, the detection described herein maybe performed by a computing system based on measured data and/or relatedbiomarkers generated by the respiration sensing system.

An oxygen saturation sensing system may measure oxygen saturation data,including light absorption, light transmission, and/or lightreflectance. The oxygen saturation sensing system may use pulseoximetry. For example, the oxygen saturation sensing system may usepulse oximetry by measuring the absorption spectra of deoxygenated andoxygenated hemoglobin. The oxygen saturation sensing system may includeone or more light-emitting diodes (LEDs) with predetermined wavelengths.The LEDs may impose light on hemoglobin. The oxygen saturation sensingsystem may measure the amount of imposed light absorbed by thehemoglobin. The oxygen saturation sensing system may measure the amountof transmitted light and/or reflected light from the imposed lightwavelengths. The oxygen saturation sensing system may include a wearabledevice, including an earpiece and/or a watch. The oxygen saturationsensing system may process the measured oxygen saturation data locallyor transmit the data to a processing unit and/or computing system.

Based on the oxygen saturation data, the oxygen saturation sensingsystem may calculate oxygen saturation-related biomarkers includingperipheral blood oxygen saturation (SpO2), hemoglobin oxygenconcentration, and/or changes in oxygen saturation rates. For example,the oxygen saturation sensing system may calculate SpO2 using the ratioof measured light absorbances of each imposed light wavelength.

Based on the oxygen saturation data, the oxygen saturation sensingsystem may predict oxygen saturation-related biomarkers, complications,and/or contextual information including cardiothoracic performance,delirium, collapsed lung, and/or recovery rates. For example, the oxygensaturation sensing system may detect post-operation delirium when thesensing system measures pre-operation SpO2 values below 59.5%. Forexample, an oxygen saturation sensing system may help monitorpost-operation patient recovery. Low SpO2may reduce the repair capacityof tissues because low oxygen may reduce the amount of energy a cell canproduce. For example, the oxygen saturation sensing system may detect acollapsed lung based on low post-operation oxygen saturation. In anexample, the detection described herein may be performed by a computingsystem based on measured data and/or related biomarkers generated by theoxygen saturation sensing system.

A blood pressure sensing system may measure blood pressure dataincluding blood vessel diameter, tissue volume, and/or pulse transittime. The blood pressure sensing system may measure blood pressure datausing oscillometric measurements, ultrasound patches,photoplethysmography, and/or arterial tonometry. The blood pressuresensing system using photoplethysmography may include a photodetector tosense light scattered by imposed light from an optical emitter. Theblood pressure sensing system using arterial tonometry may use arterialwall applanation. The blood pressure sensing system may include aninflatable cuff, wristband, watch and/or ultrasound patch.

Based on the measured blood pressure data, a blood pressure sensingsystem may quantify blood pressure-related biomarkers including systolicblood pressure, diastolic blood pressure, and/or pulse transit time. Theblood pressure sensing system may use the blood pressure-relatedbiomarkers to detect blood pressure-related conditions such as abnormalblood pressure. The blood pressure sensing system may detect abnormalblood pressure when the measured systolic and diastolic blood pressuresfall outside the range of 90/60 to 120-90 (systolic/diastolic). Forexample, the blood pressure sensing system may detect post-operationseptic or hypovolemic shock based on measured low blood pressure. Forexample, the blood pressure sensing system may detect a risk of edemabased on detected high blood pressure. The blood pressure sensing systemmay predict the required seal strength of a harmonic seal based onmeasured blood pressure data. Higher blood pressure may require astronger seal to overcome bursting. The blood pressure sensing systemmay display blood pressure information locally or transmit the data to asystem. The sensing system may display blood pressure informationgraphically over a period of time.

A blood pressure sensing system may process the blood pressure datalocally or transmit the data to a processing unit and/or a computingsystem. In an example, the detection, prediction and/or determinationdescribed herein may be performed by a computing system based onmeasured data and/or related biomarkers generated by the blood pressuresensing system.

A blood sugar sensing system may measure blood sugar data includingblood glucose level and/or tissue glucose level. The blood sugar sensingsystem may measure blood sugar data non-invasively. The blood sugarsensing system may use an earlobe clip. The blood sugar sensing systemmay display the blood sugar data.

Based on the measured blood sugar data, the blood sugar sensing systemmay infer blood sugar irregularity. Blood sugar irregularity may includeblood sugar values falling outside a certain threshold of normallyoccurring values. A normal blood sugar value may include the rangebetween 70 and 120 mg/dL while fasting. A normal blood sugar value mayinclude the range between 90 and 160 mg/dL while not-fasting.

For example, the blood sugar sensing system may detect a low fastingblood sugar level when blood sugar values fall below 50 mg/dL. Forexample, the blood sugar sensing system may detect a high fasting bloodsugar level when blood sugar values exceed 315 mg/dL. Based on themeasured blood sugar levels, the blood sugar sensing system may detectblood sugar-related biomarkers, complications, and/or contextualinformation including diabetes-associated peripheral arterial disease,stress, agitation, reduced blood flow, risk of infection, and/or reducedrecovery times.

The blood sugar sensing system may process blood sugar data locally ortransmit the data to a processing unit and/or computing system. In anexample, the detection, prediction and/or determination described hereinmay be performed by a computing system based on measured data and/orrelated biomarkers generated by the blood sugar sensing system.

A heart rate variability (HRV) sensing system may measure HRV dataincluding heartbeats and/or duration between consecutive heartbeats. TheHRV sensing system may measure HRV data electrically or optically. TheHRV sensing system may measure heart rate variability data electricallyusing ECG traces. The HRV sensing system may use ECG traces to measurethe time period variation between R peaks in a QRS complex. An HRVsensing system may measure heart rate variability optically using PPGtraces. The HRV sensing system may use PPG traces to measure the timeperiod variation of inter-beat intervals. The HRV sensing system maymeasure HRV data over a set time interval. The HRV sensing system mayinclude a wearable device, including a ring, watch, wristband, and/orpatch.

Based on the HRV data, an HRV sensing system may detect HRV-relatedbiomarkers, complications, and/or contextual information includingcardiovascular health, changes in HRV, menstrual cycle, meal monitoring,anxiety levels, and/or physical activity. For example, an HRV sensingsystem may detect high cardiovascular health based on high HRV. Forexample, an HRV sensing system may predict pre-operative stress, and usepre-operative stress to predict post-operative pain. For example, an HRVsensing system may indicate post-operative infection or sepsis based ona decrease in HRV.

The HRV sensing system may locally process HRV data or transmit the datato a processing unit and/or a computing system. In an example, thedetection, prediction, and/or determination described herein may beperformed by a computing system based on measured data and/or relatedbiomarkers generated by the HRV sensing system.

A potential of hydrogen (pH) sensing system may measure pH dataincluding blood pH and/or sweat pH. The pH sensing system may measure pHdata invasively and/or non-invasively. The pH sensing system may measurepH data non-invasively using a colorimetric approach and pH sensitivedyes in a microfluidic circuit. In a colorimetric approach, pH sensitivedyes may change color in response to sweat pH. The pH sensing system maymeasure pH using optical spectroscopy to match color change in pHsensitive dyes to a pH value. The pH sensing system may include awearable patch. The pH sensing system may measure pH data duringphysical activity.

Based on the measured pH data, the pH sensing system may detectpH-related biomarkers, including normal blood pH, abnormal blood pH,and/or acidic blood pH. The pH sensing system may detect pH-relatedbiomarkers, complications, and/or contextual information by comparingmeasured pH data to a standard pH scale. A standard pH scale mayidentify a healthy pH range to include values between 7.35 and 7.45.

The pH sensing system may use the pH-related biomarkers to indicate pHconditions including post-operative internal bleeding, acidosis, sepsis,lung collapse, and/or hemorrhage. For example, the pH sensing system maypredict post-operative internal bleeding based on pre-operation acidicblood pH. Acidic blood may reduce blood clotting capacity by inhibitingthrombin generation. For example, the pH sensing system may predictsepsis and/or hemorrhage based on acidic pH. Lactic acidosis may causeacidic pH. The pH sensing system may continuously monitor blood pH dataas acidosis may only occur during exercise.

The pH sensing system may locally process pH data or transmit pH data toa processing unit and/or computing system. In an example, the detection,prediction and/or determination described herein may be performed by acomputing system based on measured data and/or related biomarkersgenerated by the pH sensing system.

A hydration state sensing system may measure hydration data includingwater light absorption, water light reflection, and/or sweat levels. Thehydration state sensing system may use optical spectroscopy orsweat-based colorimetry. The hydration state sensing system may useoptical spectroscopy by imposing emitted light onto skin and measuringthe reflected light. Optical spectroscopy may measure water content bymeasuring amplitudes of the reflected light from certain wavelengths,including 1720 nm, 1750 nm, and/or 1770 nm. The hydration state sensingsystem may include a wearable device that may impose light onto skin.The wearable device may include a watch. The hydration state sensingsystem may use sweat-based colorimetry to measure sweat levels.Sweat-based colorimetry may be processed in conjunction with useractivity data and/or user water intake data.

Based on the hydration data, the hydration state sensing system maydetect water content. Based on the water content, a hydration statesensing system may identify hydration-related biomarkers, complications,and/or contextual information including dehydration, risk of kidneyinjury, reduced blood flow, risk of hypovolemic shock during or aftersurgery, and/or decreased blood volume.

For example, the hydration state sensing system, based on identifiedhydration, may detect health risks. Dehydration may negatively impactoverall health. For example, the hydration state sensing system maypredict risk of post-operation acute kidney injury when it detectsreduced blood flow resulting from low hydration levels. For example, thehydration state sensing system may calculate the risk of hypovolemicshock during or after surgery when the sensing system detectsdehydration or decreased blood volume. The hydration state sensingsystem may use the hydration level information to provide context forother received biomarker data, which may include heart rate. Thehydration state sensing system may measure hydration state datacontinuously. Continuous measurement may consider various factors,including exercise, fluid intake, and/or temperature, which mayinfluence the hydration state data.

The hydration state sensing system may locally process hydration data ortransmit the data to a processing unit and/or computing system. In anexample, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the hydration state sensingsystem.

A heart rate sensing system may measure heart rate data including heartchamber expansion, heart chamber contraction, and/or reflected light.The heart rate sensing system may use ECG and/or PPG to measure heartrate data. For example, the heart rate sensing system using ECG mayinclude a radio transmitter, receiver, and one or more electrodes. Theradio transmitter and receiver may record voltages across electrodespositioned on the skin resulting from expansion and contraction of heartchambers. The heart rate sensing system may calculate heart rate usingmeasured voltage. For example, the heart rate sensing system using PPGmay impose green light on skin and record the reflected light in aphotodetector. The heart rate sensing system may calculate heart rateusing the measured light absorbed by the blood over a period of time.The heart rate sensing system may include a watch, a wearable elasticband, a skin patch, a bracelet, garments, a wrist strap, an earphone,and/or a headband. For example, the heart rate sensing system mayinclude a wearable chest patch. The wearable chest patch may measureheart rate data and other vital signs or critical data includingrespiratory rate, skin temperature, body posture, fall detection,single-lead EGG, R-R intervals, and step counts. The wearable chestpatch may locally process heart rate data or transmit the data to aprocessing unit. The processing unit may include a display.

Based on the measured heart rate data, the heart rate sensing system maycalculate heart rate-related biomarkers including heart rate, heart ratevariability, and/or average heart rate. Based on the heart rate data,the heart rate sensing system may detect biomarkers, complications,and/or contextual information including stress, pain, infection, and/orsepsis. The heart rate sensing system may detect heart rate conditionswhen heart rate exceeds a normal threshold. A normal threshold forheartrate may include the range of 60 to 100 heartbeats per minute. Theheart rate sensing system may diagnose post-operation infection, sepsis,or hypovolemic shock based on increased heart rate, including heart ratein excess of 90 beats per minute.

The heart rate sensing system may process heart rate data locally ortransmit the data to a processing unit and/or computing system. In anexample, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the heart rate sensing system. Aheart rate sensing system may transmit the heart rate information to acomputing system, such as a surgical hub. The computing system maycollect and display cardiovascular parameter information including heartrate, respiration, temperature, blood pressure, arrhythmia, and/oratrial fibrillation. Based on the cardiovascular parameter information,the computing system may generate a cardiovascular health score.

A skin conductance sensing system may measure skin conductance dataincluding electrical conductivity. The skin conductance sensing systemmay include one or more electrodes. The skin conductance sensing systemmay measure electrical conductivity by applying a voltage across theelectrodes. The electrodes may include silver or silver chloride. Theskin conductance sensing system may be placed on one or more fingers.For example, the skin conductance sensing system may include a wearabledevice. The wearable device may include one or more sensors. Thewearable device may attach to one or more fingers. Skin conductance datamay vary based on sweat levels.

The skin conductance sensing system may locally process skin conductancedata or transmit the data to a computing system. Based on the skinconductance data, a skin conductance sensing system may calculate skinconductance-related biomarkers including sympathetic activity levels.For example, a skin conductance sensing system may detect highsympathetic activity levels based on high skin conductance.

A peripheral temperature sensing system may measure peripheraltemperature data including extremity temperature. The peripheraltemperature sensing system may include a thermistor, thermoelectriceffect, or infrared thermometer to measure peripheral temperature data.For example, the peripheral temperature sensing system using athermistor may measure the resistance of the thermistor. The resistancemay vary as a function of temperature. For example, the peripheraltemperature sensing system using the thermoelectric effect may measurean output voltage. The output voltage may increase as a function oftemperature. For example, the peripheral temperature sensing systemusing an infrared thermometer may measure the intensity of radiationemitted from a body's blackbody radiation. The intensity of radiationmay increase as a function of temperature.

Based on peripheral temperature data, the peripheral temperature sensingsystem may determine peripheral temperature-related biomarkers includingbasal body temperature, extremity skin temperature, and/or patterns inperipheral temperature. Based on the peripheral temperature data, theperipheral temperature sensing system may detect conditions includingdiabetes.

The peripheral temperature sensing system may locally process peripheraltemperature data and/or biomarkers or transmit the data to a processingunit. For example, the peripheral temperature sensing system may sendperipheral temperature data and/or biomarkers to a computing system,such as a surgical hub. The computing system may analyze the peripheraltemperature information with other biomarkers, including core bodytemperature, sleep, and menstrual cycle. For example, the detection,prediction, and/or determination described herein may be performed by acomputing system based on measured data and/or related biomarkersgenerated by the peripheral temperature sensing system.

A tissue perfusion pressure sensing system may measure tissue perfusionpressure data including skin perfusion pressure. The tissue perfusionsensing system may use optical methods to measure tissue perfusionpressure data. For example, the tissue perfusion sensing system mayilluminate skin and measure the light transmitted and reflected todetect changes in blood flow. The tissue perfusion sensing system mayapply occlusion. For example, the tissue perfusion sensing system maydetermine skin perfusion pressure based on the measured pressure used torestore blood flow after occlusion. The tissue perfusion sensing systemmay measure the pressure to restore blood flow after occlusion using astrain gauge or laser doppler flowmetry. The measured change infrequency of light caused by movement of blood may directly correlatewith the number and velocity of red blood cells, which the tissueperfusion pressure sensing system may use to calculate pressure. Thetissue perfusion pressure sensing system may monitor tissue flaps duringsurgery to measure tissue perfusion pressure data.

Based on the measured tissue perfusion pressure data, the tissueperfusion pressure sensing system may detect tissue perfusionpressure-related biomarkers, complications, and/or contextualinformation including hypovolemia, internal bleeding, and/or tissuemechanical properties. For example, the tissue perfusion pressuresensing system may detect hypovolemia and/or internal bleeding based ona drop in perfusion pressure. Based on the measured tissue perfusionpressure data, the tissue perfusion pressure sensing system may informsurgical tool parameters and/or medical procedures. For example, thetissue perfusion pressure sensing system may determine tissue mechanicalproperties using the tissue perfusion pressure data. Based on thedetermined mechanical properties, the sensing system may generatestapling procedure and/or stapling tool parameter adjustment(s). Basedon the determined mechanical properties, the sensing system may informdissecting procedures. Based on the measured tissue perfusion pressuredata, the tissue perfusion pressure sensing system may generate a scorefor overall adequacy of perfusion.

The tissue perfusion pressure sensing system may locally process tissueperfusion pressure data or transmit the data to a processing unit and/orcomputing system. In an example, the detection, prediction,determination, and/or generation described herein may be performed by acomputing system based on measured data and/or related biomarkersgenerated by the tissue perfusion pressure sensing system.

A coughing and sneezing sensing system may measure coughing and sneezingdata including coughing, sneezing, movement, and sound. The coughing andsneezing sensing system may track hand or body movement that may resultfrom a user covering her mouth while coughing or sneezing. The sensingsystem may include an accelerometer and/or a microphone. The sensingsystem may include a wearable device. The wearable device may include awatch.

Based on the coughing and sneezing data, the sensing system may detectcoughing and sneezing-related biomarkers, including but not limited to,coughing frequency, sneezing frequency, coughing severity, and/orsneezing severity. The sensing system may establish a coughing andsneezing baseline using the coughing and sneezing information. Thecoughing and sneezing sensing system may locally process coughing andsneezing data or transmit the data to a computing system.

Based on the coughing and sneezing data, the sensing system may detectcoughing and sneezing-related biomarkers, complications, and/orcontextual information including respiratory tract infection, infection,collapsed lung, pulmonary edema, gastroesophaegeal reflux disease,allergic rhinitis, and/or systemic inflammation. For example, thecoughing and sneezing sensing system may indicate gastroesophagealreflux disease when the sensing system measures chronic coughing.Chronic coughing may lead to inflammation of the lower esophagus. Loweresophagus inflammation may affect the properties of stomach tissue forsleeve gastrectomy. For example, the coughing and sneezing sensingsystem may detect allergic rhinitis based on sneezing. Sneezing may linkto systemic inflammation. Systemic inflammation may affect themechanical properties of the lungs and/or other tissues. In an example,the detection, prediction, and/or determination described herein may beperformed by a computing system based on measured data and/or relatedbiomarkers generated by the coughing and sneezing sensing system.

A gastrointestinal (GI) motility sensing system may measure GI motilitydata including pH, temperature, pressure, and/or stomach contractions.The GI motility sensing system may use electrogastrography,electrogastroenterography, stethoscopes, and/or ultrasounds. The GImotility sensing system may include a non-digestible capsule. Forexample, the ingestible sensing system may adhere to the stomach lining.The ingestible sensing system may measure contractions using apiezoelectric device which generates a voltage when deformed.

Based on the GI data, the sensing system may calculate GImotility-related biomarkers including gastric, small bowel, and/orcolonic transit times. Based on the gastrointestinal motilityinformation, the sensing system may detect GI motility-relatedconditions including ileus. The GI motility sensing system may detectileus based on a reduction in small bowel motility. The GI motilitysensing system may notify healthcare professionals when it detects GImotility conditions. The GI motility sensing system may locally processGI motility data or transmit the data to a processing unit, in anexample, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the GI motility sensing system.

A GI tract imaging/sensing system may collect images of a patient'scolon. The GI tract imaging/sensing system may include an ingestiblewireless camera and a receiver. The GI tract imaging/sensing system mayinclude one or more white LEDs, a battery, radio transmitter, andantenna. The ingestible camera may include a pill. The ingestible cameramay travel through the digestive tract and take pictures of the colon.The ingestible camera may take pictures up to 35 frames per secondduring motion. The ingestible camera may transmit the pictures to areceiver. The receiver may include a wearable device. The GI tractimaging/sensing system may process the images locally or transmit themto a processing unit. Doctors may look at the raw images to make adiagnosis.

Based on the GI tract images, the GI tract imaging sensing system mayidentify GI tract-related biomarkers including stomach tissue mechanicalproperties or colonic tissue mechanical properties. Based on thecollected images, the GI tract imaging sensing system may detect GItract-related biomarkers, complications, and/or contextual informationincluding mucosal inflammation, Crohn's disease, anastomotic leak,esophagus inflammation, and/or stomach inflammation. The GI tractimaging/sensing system may replicate a physician diagnosis using imageanalysis software. The GI tract imaging/sensing system may locallyprocess images or transmit the data, to a processing unit. In anexample, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the GI tract imaging/sensingsystem.

A respiratory tract bacteria sensing system may measure bacteria dataincluding foreign DNA or bacteria. The respiratory tract bacteriasensing system may use a radio frequency identification (RFID) tagand/or electronic nose (e-nose). The sensing system using an RFID tagmay include one or more gold electrodes, graphene sensors, and/or layersof peptides. The RFID tag may bind to bacteria. When bacteria bind tothe RFID tag, the graphene sensor may detect a change insignal-to-signal presence of bacteria. The RIFID tag may include animplant. The implant may adhere to a tooth. The implant may transmitbacteria data. The sensing system may use a portable e-nose to measurebacteria data.

Based on measured bacteria data, the respiratory tract bacteria sensingsystem may detect bacteria-related biomarkers including bacteria levels.Based on the bacteria data, the respiratory tract bacteria sensingsystem may generate an oral health score. Based on the detected bacteriadata, the respiratory tract bacteria sensing system may identifybacteria-related biomarkers, complications, and/or contextualinformation, including pneumonia, lung infection, and/or lunginflammation. The respiratory tract bacteria sensing system may locallyprocess bacteria information or transmit the data to a processing unit.In an example, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the respiratory tract bacteriasensing system.

An edema sensing system may measure edema data including lower legcircumference, leg volume, and/or leg water content level. The edemasensing system may include a force sensitive resistor, strain gauge,accelerometer, gyroscope, magnetometer, and/or ultrasound. The edemasensing system may include a wearable device. For example, the edemasensing system may include socks, stockings, and/or ankle bands.

Based on the measured edema data, the edema sensing system may detectedema-related biomarkers, complications, and/or contextual information,including inflammation, rate of change in inflammation, poor healing,infection, leak, colorectal anastomotic leak, and/or water build-up.

For example, the edema sensing system may detect a risk of colorectalanastomotic leak based on fluid build-up. Based on the detected edemaphysiological conditions, the edema sensing system may generate a scorefor healing quality. For example, the edema sensing system may generatethe healing quality score by comparing edema information to a certainthreshold lower leg circumference. Based on the detected edemainformation, the edema sensing system may generate edema tool parametersincluding responsiveness to stapler compression. The edema sensingsystem may provide context for measured edema data by using measurementsfrom the accelerometer, gyroscope, and/or magnetometer. For example, theedema sensing system may detect whether the user is sitting, standing,or lying down.

The edema sensing system may process measured edema data locally ortransmit the edema data to a processing unit. In an example, thedetection, prediction, and/or determination described herein may beperformed by a computing system based on measured data and/or relatedbiomarkers generated by the edema sensing system.

A mental aspect sensing system may measure mental aspect data, includingheart rate, heart rate variability, brain activity, skin conductance,skin temperature, galvanic skin response, movement, and/or sweat rate.The mental aspect sensing system may measure mental aspect data over aset duration to detect changes in mental aspect data. The mental aspectsensing system may include a wearable device. The wearable device mayinclude a wristband.

Based on the mental aspect data, the sensing system may detect mentalaspect-related biomarkers, including emotional patterns, positivitylevels, and/or optimism levels. Based on the detected mental aspectinformation, the mental aspect sensing system may identify mentalaspect-related biomarkers, complications, and/or contextual informationincluding cognitive impairment, stress, anxiety, and/or pain. Based onthe mental aspect information, the mental aspect sensing system maygenerate mental aspect scores, including a positivity score, optimismscore, confusion or delirium score, mental acuity score, stress score,anxiety score, depression score, and/or pain score.

Mental aspect data, related biomarkers, complications, contextualinformation, and/or mental aspect scores may be used to determinetreatment courses, including pain relief therapies. For example,post-operative pain may be predicted when it detects pre-operativeanxiety and/or depression. For example, based on detected positivity andoptimism levels, the mental aspect sensing system may determine moodquality and mental state. Based on mood quality and mental state, themental aspect sensing system may indicate additional care proceduresthat would benefit a patient, including paint treatments and/orpsychological assistance. For example, based on detected cognitiveimpairment, confusion, and/or mental acuity, the mental aspects sensingsystem may indicate conditions including delirium, encephalopathy,and/or sepsis. Delirium may be hyperactive or hypoactive. For example,based on detected stress and anxiety, the mental aspect sensing systemmay indicate conditions including hospital anxiety and/or depression.Based on detected hospital anxiety and/or depression, the mental aspectsensing system may generate a treatment plan, including pain relieftherapy and/or pre-operative support.

In an example, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the mental aspect sensing system.The mental aspect sensing system may process mental aspect data locallyor transmit the data to a processing unit.

A sweat sensing system may measure sweat data including sweat, sweatrate, cortisol, adrenaline, and/or lactate. The sweat sensing system maymeasure sweat data using microfluidic capture, saliva testing,nanoporous electrode systems, e-noses, reverse iontophoresis, bloodtests, amperometric thin film biosensors, textile organicelectrochemical transistor devices, and/or electrochemical biosensors.The sensing system may measure sweat data with microfluidic captureusing a colorimetric or impedimetric method. The microfluidic capturemay include a flexible patch placed in contact with skin. The sweatsensing system may measure cortisol using saliva tests. The saliva testsmay use electrochemical methods and/or molecularly selective organicelectrochemical transistor devices. The sweat sensing system may measureion build-up that bind to cortisol in sweat to calculate cortisollevels. The sweat sensing system may use enzyme reactions to measurelactate. Lactate may be measured using lactate oxidase and/or lactatedehydrogenase methods.

Based on the measured sweat data, the sweat sensing system or processingunit may detect sweat related biomarkers, complications, and/orcontextual information including cortisol levels, adrenaline levels,and/or lactate levels. Based on the detected sweat data and/or relatedbiomarkers, the sweat sensing system may indicate sweat physiologicalconditions including sympathetic nervous system activity, psychologicalstress, cellular immunity, circadian rhythm, blood pressure, tissueoxygenation, and/or post-operation pain. For example, based on sweatrate data, the sweat sensing system may detect psychological stress.Based on the detected psychological stress, the sweat sensing system mayindicate heightened sympathetic activity. Heightened sympatheticactivity may indicate post-operation pain.

Based on the detected sweat information, the sweat sensing system maydetect sweat-related biomarkers, complications, and/or contextualinformation including post-operation infection, metastasis, chronicelevation, ventricular failure, sepsis, hemorrhage, hyperlactemia,and/or septic shock. For example, the sensing system may detect septicshock when serum lactate concentration exceeds a certain level, such as2 mmol/L. For example, based on detected patterns of adrenaline surges,the sweat sensing system may indicate a risk of heart attack and/orstroke. For example, surgical tool parameter adjustments may bedetermined based on detected adrenaline levels. The surgical toolparameter adjustments may include settings for surgical sealing tools.For example, the sweat sensing system may predict infection risk and/ormetastasis based on detected cortisol levels. The sweat sensing systemmay notify healthcare professionals about the condition.

In an example, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the sweat sensing system. Thesweat sensing system may locally process sweat data or transmit thesweat data to a processing unit.

A circulating tumor cell sensing system may detect circulating tumorcells. The circulating tumor cell sensing system may detect circulatingtumor cells using an imaging agent. The imaging agent may usemicrobubbles attached with antibodies which target circulating tumorcells. The imaging agent may be injected into the bloodstream. Theimaging agent may attach to circulating tumor cells. The circulatingtumor cell sensing system may include an ultrasonic transmitter andreceiver. The ultrasonic transmitter and receiver may detect the imagingagent attached to circulating tumor cells. The circulating tumor cellsensing system may receive circulating tumor cell data.

Based on the detected circulating tumor cells data, the circulatingtumor cell sensing system may calculate metastatic risk. The presence ofcirculating cancerous cells may indicate metastatic risk. Circulatingcancerous cells per milliliter of blood exceeding a threshold amount mayindicate a metastatic risk. Cancerous cells may circulate thebloodstream when tumors metastasize. Based on the calculated metastaticrisk, the circulating tumor cell sensing system may generate a surgicalrisk score. Based on the generated surgical risk score, the circulatingtumor cell sensing system may indicate surgery viability and/orsuggested surgical precautions.

In an example, the detection, prediction and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the circulating tumor cellssensing system. The circulating tumor cell sensing system may processthe circulating tumor cell data locally or transmit the circulatingtumor cells data to a processing unit.

An autonomic tone sensing system may measure autonomic tone dataincluding skin conductance, heart rate variability, activity, and/orperipheral body temperature. The autonomic tone sensing system mayinclude one or more electrodes, PPG trace, ECG trace, accelerometer,GPS, and/or thermometer. The autonomic tone sensing system may include awearable device that may include a wristband and/or finger band.

Based on the autonomic tone data, the autonomic tone sensing system maydetect autonomic tone related biomarkers, complications, and/orcontextual information, including sympathetic nervous system activitylevel and/or parasympathetic nervous system activity level. Theautonomic tone may describe the basal balance between the sympatheticand parasympathetic nervous system. Based on the measured autonomic tonedata, the autonomic tone sensing system may indicate risk forpost-operative conditions including inflammation and/or infection. Highsympathetic activity may associate with increase in inflammatorymediators, suppressed immune function, postoperative ileus, increasedheart rate, increased skin conductance, increased sweat rate, and/oranxiety.

In an example, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the autonomic tone sensingsystem. The autonomic tone sensing system may process the autonomic tonedata locally or transmit the data to a processing unit.

A circadian rhythm sensing system may measure circadian rhythm dataincluding light exposure, heart rate, core body temperature, cortisollevels, activity, and/or sleep. Based on the circadian rhythm data thecircadian rhythm sensing system may detect circadian rhythm-relatedbiomarkers, complications, and/or contextual information including sleepcycle, wake cycle, circadian patterns, disruption in circadian rhythm,and/or hormonal activity.

For example, based on the measured circadian rhythm data, the circadianrhythm sensing system may calculate the start and end of the circadiancycle. The circadian rhythm sensing system may indicate the beginning ofthe circadian day based on measured cortisol. Cortisol levels may peakat the start of a circadian day. The circadian rhythm sensing system mayindicate the end of the circadian day based on measured heart rateand/or core body temperature. Heart rate and/or core body temperaturemay drop at the end of a circadian day. Based on the circadianrhythm-related biomarkers, the sensing system or processing unit maydetect conditions including risk of infection and/or pain. For example,disrupted circadian rhythm may indicate pain and discomfort.

In an example, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the circadian rhythm sensingsystem. The circadian rhythm sensing system may process the circadianrhythm data locally or transmit the data to a processing unit.

A menstrual cycle sensing system may measure menstrual cycle dataincluding heart rate, heart rate variability, respiration rate, bodytemperature, and/or skin perfusion. Based on the menstrual cycle data,the menstrual cycle unit may indicate menstrual cycle-relatedbiomarkers, complications, and/or contextual information, includingmenstrual cycle phase. For example, the menstrual cycle sensing systemmay detect the periovulatory phase in the menstrual cycle based onmeasured heart rate variability. Changes in heart rate variability mayindicate the periovulatory phase. For example, the menstrual cyclesensing system may detect the luteal phase in the menstrual cycle basedon measured wrist skin temperature and/or skin perfusion. Increasedwrist skin temperature may indicate the luteal phase. Changes in skinperfusion may indicate the luteal phase. For example, the menstrualcycle sensing system may detect the ovulatory phase based on measuredrespiration rate. Low respiration rate may indicate the ovulatory phase.

Based on menstrual cycle-related biomarkers, the menstrual cycle sensingsystem may determine conditions including hormonal changes, surgicalbleeding, scarring, bleeding risk, and/or sensitivity levels. Forexample, the menstrual cycle phase may affect surgical bleeding inrhinoplasty. For example, the menstrual cycle phase may affect healingand scarring in breast surgery. For example, bleeding risk may decreaseduring the periovulatory phase in the menstrual cycle.

In an example, the detection, prediction, and/or determination describedherein may be performed by a computing system based on measured dataand/or related biomarkers generated by the menstrual cycle sensingsystem. The menstrual cycle sensing system may locally process menstrualcycle data or transmit the data to a processing unit.

An environmental sensing system may measure environmental data includingenvironmental temperature, humidity, mycotoxin spore count, and airbornechemical data. The environmental sensing system may include a digitalthermometer, air sampling, and/or chemical sensors. The sensing systemmay include a wearable device. The environmental sensing system may usea digital thermometer to measure environmental temperature and/orhumidity. The digital thermometer may include a metal strip with adetermined resistance. The resistance of the metal strip may vary withenvironmental temperature. The digital thermometer may apply the variedresistance to a calibration curve to determine temperature. The digitalthermometer may include a wet bulb and a dry bulb. The wet bulb and drybulb may determine a difference in temperature, which then may be usedto calculate humidity.

The environmental sensing system may use air sampling to measuremycotoxin spore count. The environmental sensing system may include asampling plate with adhesive media connected to a pump. The pump maydraw air over the plate over set time at a specific flow rate. The settime may last up to 10 minutes. The environmental sensing system mayanalyze the sample using a microscope to count the number of spores. Theenvironmental sensing system may use different air sampling techniquesincluding high-performance liquid chromatography (HPLC), liquidchromatography-tandem mass spectrometry (LC-MS/MS), and/or immunoassaysand nanobodies.

The environmental sensing system may include chemical sensors to measureairborne chemical data. Airborne chemical data may include differentidentified airborne chemicals, including nicotine and/or formaldehyde.The chemical sensors may include an active layer and a transducer layer.The active layer may allow chemicals to diffuse into a matrix and altersome physical or chemical property. The changing physical property mayinclude refractive index and/or H-bond formation. The transducer layermay convert the physical and/or chemical variation into a measurablesignal, including an optical or electrical signal. The environmentalsensing system may include a handheld instrument. The handheldinstrument may detect and identify complex chemical mixtures thatconstitute aromas, odors, fragrances, formulations, spills, and/orleaks. The handheld instrument may include an array of nanocompositesensors. The handheld instrument may detect and identify substancesbased on chemical profile.

Based on the environmental data, the sensing system may determineenvironmental information including climate, mycotoxin spore count,mycotoxin identification, airborne chemical identification, airbornechemical levels, and/or inflammatory chemical inhalation. For example,the environmental sensing system may approximate the mycotoxin sporecount in the air based on the measured spore count from a collectedsample. The sensing system may identify the mycotoxin spores which mayinclude molds, pollens, insect parts, skin cell fragments, fibers,and/or inorganic particulate. For example, the sensing system may detectinflammatory chemical inhalation, including cigarette smoke. The sensingsystem may detect second-hand or third-hand smoke.

Based on the environmental information, the sensing system may generateenvironmental aspects conditions including inflammation, reduced lungfunction, airway hyper-reactivity, fibrosis, and/or reduce immunefunctions. For example, the environmental aspects sensing system maydetect inflammation and fibrosis based on the measured environmentalaspects information. The sensing system may generate instructions for asurgical tool, including a staple and sealing tool used in lungsegmentectomy, based on the inflammation and/or fibrosis. Inflammationand fibrosis may affect the surgical tool usage. For example, cigarettesmoke may cause higher pain scores in various surgeries.

The environmental sensing system may generate an air quality score basedon the measured mycotoxins and/or airborne chemicals. For example, theenvironmental sensing system may notify about hazardous air quality ifit detects a poor air quality score. The environmental sensing systemmay send a notification when the generated air quality score falls belowa certain threshold. The threshold may include exposure exceeding 105spores of mycotoxins per cubic meter. The environmental sensing systemmay display a readout of the environment condition exposure over time.

The environmental sensing system may locally process environmental dataor transmit the data to a processing unit. In an example, the detection,prediction, and/or determination described herein may be performed by acomputing system based on measured data generated by the environmentalsensing system.

A light exposure sensing system may measure light exposure data. Thelight exposure sensing system may include one or more photodiode lightsensors. For example, the light exposure sensing system using photodiodelight sensors may include a semiconductor device in which the devicecurrent may vary as a function of light intensity. Incident photons maycreate electron-hole pairs that flow across the semiconductor junction,which may create current. The rate of electron-hole pair generation mayincrease as a function of the intensity of the incident light. The lightexposure sensing system may include one or more photoresistor lightsensors. For example, the light exposure sensing system usingphotoresistor light sensors may include a light-dependent resistor inwhich the resistance decreases as a function of light intensity. Thephotoresistor light sensor may include passive devices without aPN-junction. The photoresistor light sensors may be less sensitive thanphotodiode light sensors. The light exposure sensing system may includea wearable, including a necklace and/or clip-on button.

Based on the measured light exposure data, the light exposure sensingsystem may detect light exposure information including exposureduration, exposure intensity, and/or light type. For example, thesensing system may determine whether light exposure consists of naturallight or artificial light. Based on the detected light exposureinformation, the light exposure sensing system may detect lightexposure-related biomarker(s) including circadian rhythm. Light exposuremay entrain the circadian cycle.

The light exposure sensing system may locally process the light exposuredata or transmit the data to a processing unit. In an example, thedetection, prediction, and/or determination described herein may beperformed by a computing system based on measured data and/or relatedbiomarkers generated by the light exposure sensing system.

The various sensing systems described herein may measure data, deriverelated biomarkers, and send the biomarkers to a computing system, suchas a surgical hub as described herein with reference to FIGS. 1-12. Thevarious sensing systems described herein may send the measured data tothe computing system. The computing system may derive the relatedbiomarkers based on the received measurement data.

The biomarker sensing systems may include a wearable device. In anexample, the biomarker sensing system may include eyeglasses. Theeyeglasses may include a nose pad sensor. The eyeglasses may measurebiomarkers, including lactate, glucose, and/or the like. In an example,the biomarker sensing system may include a mouthguard. The mouthguardmay include a sensor to measure biomarkers including uric acid and/orthe like. In an example, the biomarker sensing system may include acontact lens. The contact lens may include a sensor to measurebiomarkers including glucose and/or the like. In an example, thebiomarker sensing system may include a tooth sensor. The tooth sensormay be graphene-based. The tooth sensor may measure biomarkers includingbacteria and/or the like. In an example, the biomarker sensing systemmay include a patch. The patch may be wearable on the chest skin or armskin. For example, the patch may include a chem-phys hybrid sensor. Thechem-phys hybrid sensor may measure biomarkers including lactate, ECG,and/or the like. For example, the patch may include nanomaterials. Thenanomaterials patch may measure biomarkers including glucose and/or thelike. For example, the patch may include an iontophoretic biosensor. Theiontophoretic biosensor may measure biomarkers including glucose and/orthe like. In an example, the biomarker sensing system may include amicrofluidic sensor. The microfluidic sensor may measure biomarkersincluding lactate, glucose, and/or the like. In an example, thebiomarker sensing system may include an integrated sensor array. Theintegrated sensory array may include a wearable wristband. Theintegrated sensory array may measure biomarkers including lactate,glucose, and/or the like. In an example, the biomarker sensing systemmay include a wearable diagnostics device. The wearable diagnosticdevice may measure biomarkers including cortisol, interleukin-6, and/orthe like. In an example, the biomarker sensing system may include aself-powered textile-based biosensor. The self-powered textile-basedbiosensor may include a sock. The self-powered textile-based biosensormay measure biomarkers including lactate and/or the like.

The various biomarkers described herein may be related to variousphysiologic systems, including behavior and psychology, cardiovascularsystem, renal system, skin system, nervous system, GI system,respiratory system, endocrine system, immune system, tumor,musculoskeletal system, and/or reproductive system.

Behavior and psychology may include social interactions, diet, sleep,activity, and/or psychological status. Behavior and psychology-relatedbiomarkers, complications, contextual information, and/or conditions maybe determined and/or predicted based on analyzed biomarker sensingsystems data. A computing system, as described herein, may select one ormore biomarkers (e.g., data from biomarker sensing systems) frombehavior and psychology-related biomarkers, including sleep, circadianrhythm, physical activity, and/or mental aspects for analysis. Behaviorand psychology scores may be generated based on the analyzed biomarkers,complications, contextual information, and/or conditions. Behavior andpsychology scores may include scores for social interaction, diet,sleep, activity, and/or psychological status.

For example, based on the selected biomarker sensing systems data,sleep-related biomarkers, complications, and/or contextual informationmay be determined, including sleep quality, sleep duration, sleeptiming, immune function, and/or post-operation pain. Based on theselected biomarker sensing systems data, sleep-related conditions may bepredicted, including inflammation. In an example, inflammation may bepredicted based on analyzed pre-operation sleep. Elevated inflammationmay be determined and/or predicted based on disrupted pre-operationsleep. In an example, immune function may be determined based onanalyzed pre-operation sleep. Reduced immune function may be predictedbased on disrupted pre-operation sleep. In an example, post-operationpain may be determined based on analyzed sleep. Post-operation pain maybe determined and/or predicted based on disrupted sleep. In an example,pain and discomfort may be determined based on analyzed circadianrhythm. A compromised immune system may be determined based on analyzedcircadian rhythm cycle disruptions.

For example, based on the selected biomarker sensing systems data,activity-related biomarkers, complications, and/or contextualinformation may be determined, including activity duration, activityintensity, activity type, activity pattern, recovery time, mentalhealth, physical recovery, immune function, and/or inflammatoryfunction. Based on the selected biomarker sensing systems data,activity-related conditions may be predicted. In an example, improvedphysiology may be determined based on analyzed activity intensity.Moderate intensity exercise may indicate shorter hospital stays, bettermental health, better physical recovery, improved immune function,and/or improved inflammatory function. Physical activity type mayinclude aerobic activity and/or non-aerobic activity. Aerobic physicalactivity may be determined based on analyzed physical activity,including running, cycling, and/or weight training. Non-aerobic physicalactivity may be determined based on analyzed physical activity,including walking and/or stretching.

For example, based on the selected biomarker sensing systems data,psychological status-related biomarkers, complications, and/orcontextual information may be determined, including stress, anxiety,pain, positive emotions, abnormal states, and/or post-operative pain.Based on the selected biomarker sensing systems data, psychologicalstatus-related conditions may be predicted, including physical symptomsof disease. Higher post-operative pain may be determined and/orpredicted based on analyzed high levels of pre-operative stress,anxiety, and/or pain. Physical symptoms of disease may be predictedbased on determined high optimism.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

The cardiovascular system may include the lymphatic system, bloodvessels, blood, and/or heart. Cardiovascular system-related biomarkers,complications, contextual information, and/or conditions may bedetermined and/or predicted based on analyzed biomarker sensing systemsdata. Systemic circulation conditions may include conditions for thelymphatic system, blood vessels, and/or blood. A computing system mayselect one or more biomarkers (e.g., data from biomarker sensingsystems) from cardiovascular system-related biomarkers, including bloodpressure, VO2 max, hydration state, oxygen saturation, blood pH, sweat,core body temperature, peripheral temperature, edema, heart rate, and/orheart rate variability for analysis.

For example, based on the selected biomarker sensing systems data,lymphatic system-related biomarkers, complications, and/or contextualinformation may be determined, including swelling, lymph composition,and/or collagen deposition. Based on the selected biomarker sensingsystems data, lymphatic system-related conditions may be predicted,including fibrosis, inflammation, and/or post-operation infection.Inflammation may be predicted based on determined swelling.Post-operation infection may be predicted based on determined swelling.Collagen deposition may be determined based on predicted fibrosis.Increased collagen deposition may be predicted based on fibrosis.Harmonic tool parameter adjustments may be generated based on determinedcollagen deposition increases. Inflammatory conditions may be predictedbased on analyzed lymph composition. Different inflammatory conditionsmay be determined and/or predicted based on changes in lymph peptidomecomposition. Metastatic cell spread may be predicted based on predictedinflammatory conditions. Harmonic tool parameter adjustments and margindecisions may be generated based on predicted inflammatory conditions.

For example, based on the selected biomarker sensing systems data, bloodvessel-related biomarkers, complications, and/or contextual inflammationmay be determined, including permeability, vasomotion, pressure,structure, healing ability, harmonic sealing performance, and/orcardiothoracic health fitness. Surgical tool usage recommendationsand/or parameter adjustments may be generated based on the determinedblood vessel-related biomarkers. Based on the selected biomarker sensingsystems data, blood vessel-related conditions may be predicted,including infection, anastomotic leak, septic shock and/or hypovolemicshock. In an example, increased vascular permeability may be determinedbased on analyzed edema, bradykinin, histamine, and/or endothelialadhesion molecules. Endothelial adhesion molecules may be measured usingcell samples to measure transmembrane proteins. In an example,vasomotion may be determined based on selected biomarker sensing systemsdata. Vasomotion may include vasodilators and/or vasoconstrictors. In anexample, shock may be predicted based on the determined bloodpressure-related biomarkers, including vessel information and/or vesseldistribution. Individual vessel structure may include arterialstiffness, collagen content, and/or vessel diameter. Cardiothoracichealth fitness may be determined based on VO2 max. Higher risk ofcomplications may be determined and/or predicted based on poor VO2 max.

For example, based on the selected biomarker sensing systems data,blood-related biomarkers, complications, and/or contextual informationmay be determined, including volume, oxygen, pH, waste products,temperature, hormones, proteins, and/or nutrients. Based on the selectedbiomarker sensing systems data, blood-related complications and/orcontextual information may be determined, including cardiothoracichealth fitness, lung function, recovery capacity, anaerobic threshold,oxygen intake, carbon dioxide (CO2) production, fitness, tissueoxygenation, colloid osmotic pressure, and/or blood clotting ability.Based on derived blood-related biomarkers, blood-related conditions maybe predicted, including post-operative acute kidney injury, hypovolemicshock, acidosis, sepsis, lung collapse, hemorrhage, bleeding risk,infection, and/or anastomotic leak.

For example, post-operative acute kidney injury and/or hypovolemic shockmay be predicted based on the hydration state. For example, lungfunction, lung recovery capacity, cardiothoracic health fitness,anaerobic threshold, oxygen uptake, and CO2 product may be predictedbased on the blood-related biomarkers, including red blood cell countand/or oxygen saturation. For example, cardiovascular complications maybe predicted based on the blood-related biomarkers, including red bloodcell count and/or oxygen saturation. For example, acidosis may bepredicted based on the pH. Based on acidosis, blood-related conditionsmay be indicated, including sepsis, lung collapse, hemorrhage, and/orincreased bleeding risk. For example, based on sweat, blood-relatedbiomarkers may be derived, including tissue oxygenation. Insufficienttissue oxygenation may be predicted based on high lactate concentration.Based on insufficient tissue oxygenation, blood-related conditions maybe predicted, including hypovolemic shock, septic shock, and/or leftventricular failure. For example, based on the temperature, bloodtemperature-related biomarkers may be derived, including menstrual cycleand/or basal temperature. Based on the blood temperature-relatedbiomarkers, blood temperature-related conditions may be predicted,including sepsis and/or infection. For example, based on proteins,including albumin content, colloid osmotic pressure may be determined.Based on the colloid osmotic pressure, blood protein-related conditionsmay be predicted, including edema risk and/or anastomotic leak.Increased edema risk and/or anastomotic leak may be predicted based onlow colloid osmotic pressure. Bleeding risk may be predicted based onblood clotting ability. Blood clotting ability may be determined basedon fibrinogen content. Reduced blood clotting ability may be determinedbased on low fibrinogen content.

For example, based on the selected biomarker sensing systems data, thecomputing system may derive heart-related biomarkers, complications,and/or contextual information, including heart activity, heart anatomy,recovery rates, cardiothoracic health fitness, and/or risk ofcomplications. Heart activity biomarkers may include electrical activityand/or stroke volume. Recovery rate may be determined based on heartrate biomarkers. Reduced blood supply to the body may be determinedand/or predicted based on irregular heart rate. Slower recovery may bedetermined and/or predicted based on reduced blood supply to the body.Cardiothoracic health fitness may be determined based on analyzed VO2max values. VO2 max values below a certain threshold may indicate poorcardiothoracic health fitness. VO2 max values below a certain thresholdmay indicate a higher risk of heart-related complications.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device, based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

Renal system-related biomarkers, complications, contextual information,and/or conditions may be determined and/or predicted based on analyzedbiomarker sensing systems data. A computing system, as described herein,may select one or more biomarkers (e.g., data from biomarker sensingsystems) from renal system-related biomarkers for analysis. Based on theselected biomarker sensing systems data, renal system-relatedbiomarkers, complications, and/or contextual information may bedetermined including ureter, urethra, bladder, kidney, general urinarytract, and/or ureter fragility. Based on the selected biomarker sensingsystems data, renal system-related conditions may be predicted,including acute kidney injury, infection, and/or kidney stones. In anexample, ureter fragility may be determined based on urine inflammatoryparameters. In an example, acute kidney injury may be predicted based onanalyzed Kidney Injury Molecule-1 (KIM-1) in urine.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

The skin system may include biomarkers relating to microbiome, skin,nails, hair, sweat, and/or sebum. Skin-related biomarkers may includeepidermis biomarkers and/or dermis biomarkers. Sweat-related biomarkersmay include activity biomarkers and/or composition biomarkers. Skinsystem-related biomarkers, complications, contextual information, and/orconditions may be determined and/or predicted based on analyzedbiomarker sensing systems data. A computing system, as described herein,may select one or more biomarkers (e.g., data from biomarker sensingsystems) from skin-related biomarkers, including skin conductance, skirtperfusion pressure, sweat, autonomic tone, and/or pH for analysis.

For example, based on selected biomarker sensing systems data,skin-related biomarkers, complications, and/or contextual informationmay be determined, including color, lesions, trans-epidermal water loss,sympathetic nervous system activity, elasticity, tissue perfusion,and/or mechanical properties. Stress may be predicted based ondetermined skin conductance. Skin conductance may act as a proxy forsympathetic nervous system activity. Sympathetic nervous system activitymay correlate with stress. Tissue mechanical properties may bedetermined based on skin perfusion pressure. Skin perfusion pressure mayindicate deep tissue perfusion. Deep tissue perfusion may determinetissue mechanical properties. Surgical tool parameter adjustments may begenerated based on determined tissue mechanical properties.

Based on selected biomarker sensing systems data, skin-relatedconditions may be predicted.

For example, based on selected biomarker sensing systems data,sweat-related biomarkers, complications, and/or contextual informationmay be determined, including activity, composition, autonomic tone,stress response, inflammatory response, blood pH, blood vessel health,immune function, circadian rhythm, and/or blood lactate concentration.Based on selected biomarker sensing systems data, sweat-relatedconditions may be predicted, including ileus, cystic fibrosis, diabetes,metastasis, cardiac issues, and/or infections.

For example, sweat composition-related biomarkers may be determinedbased on selected biomarker data. Sweat composition biomarkers mayinclude proteins, electrolytes, and/or small molecules. Based on thesweat composition biomarkers, skin system complications, conditions,and/or contextual information may be predicted, including ileus, cysticfibrosis, acidosis, sepsis, lung collapse, hemorrhage, bleeding risk,diabetes, metastasis, and/or infection. For example, based on proteinbiomarkers, including sweat neuropeptide Y and/or sweat antimicrobials,stress response may be predicted. Higher sweat neuropeptide Y levels mayindicate greater stress response. Cystic fibrosis and/or acidosis may bepredicted based on electrolyte biomarkers, including chloride ions, pH,and other electrolytes. High lactate concentrations may be determinedbased on blood pH. Acidosis may be predicted based on high lactateconcentrations. Sepsis, lung collapse, hemorrhage, and/or bleeding riskmay be predicted based on predicted acidosis. Diabetes, metastasis,and/or infection may be predicted based on small molecule biomarkers.Small molecule biomarkers may include blood sugar and/or hormones.Hormone biomarkers may include adrenaline and/or cortisol. Based onpredicted metastasis, blood vessel health may be determined. Infectiondue to lower immune function may be predicted based on detectedcortisol. Lower immune function may be determined and/or predicted basedon high cortisol. For example, sweat-related conditions, includingstress response, inflammatory response, and/or ileus, may be predictedbased on determined autonomic tone. Greater stress response, greaterinflammatory response, and/or ileus may be determined and/or predictedbased on high sympathetic tone.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

Nervous system-related biomarkers, complications, contextualinformation, and/or conditions may be determined and/or predicted basedon analyzed biomarker sensing systems data. A computing system, asdescribed herein, may select one or more biomarkers (e.g., data frombiomarker sensing systems) from nervous system-related biomarkers,including circadian rhythm, oxygen saturation, autonomic tone, sleep,activity, and/or mental aspects for. The nervous system may include thecentral nervous system (CNS) and/or the peripheral nervous system. TheCNS may include brain and/or spinal cord. The peripheral nervous systemmay include the autonomic nervous system, motor system, enteric system,and/or sensory system.

For example, based on the selected biomarker sensing systems data,CNS-related biomarkers, complications, and/or contextual information maybe determined, including post-operative pain, immune function, mentalhealth, and/or recovery rate. Based on the selected biomarker sensingsystems data, CNS-related conditions may be predicted, includinginflammation, delirium, sepsis, hyperactivity, hypoactivity, and/orphysical symptoms of disease. In an example, a compromised immune systemand/or high pain score may be predicted based on disrupted sleep. In anexample, post-operation delirium may be predicted based on oxygensaturation. Cerebral oxygenation may indicate post-operation delirium.

For example, based on the selected biomarker sensing systems data,peripheral nervous system-related biomarkers, complications, and/orcontextual information may be determined. Based on the selectedbiomarker sensing systems data, peripheral nervous system-relatedconditions may be predicted, including inflammation and/or ileus. In anexample, high sympathetic tone may be predicted based on autonomic tone.Greater stress response may be predicted based on high sympathetic tone.Inflammation and/or ileus may be predicted based on high sympathetictone.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

The GI system may include the upper GI tract, lower GI tract, ancillaryorgans, peritoneal space, nutritional states, and microbiomes. The upperGI may include the mouth, esophagus, and/or stomach. The lower GI mayinclude the small intestine, colon, and/or rectum. Ancillary organs mayinclude pancreas, liver, spleen, and/or gallbladder. Peritoneal spacemay include mesentry and/or adipose blood vessels. Nutritional statesmay include short-term, long-term, and/or systemic. GI-relatedbiomarkers, complications, contextual information, and/or conditions maybe determined and/or predicted based on analyzed biomarker sensingsystems data. A computing system, as described herein, may select one ormore biomarkers (e.g., data from biomarker sensing systems) fromGI-related biomarkers, including coughing and sneezing, respiratorybacteria, GI tract imaging/sensing, GI motility, pH, tissue perfusionpressure, environmental, and/or alcohol consumption for analysis.

The upper GI may include the mouth, esophagus, and/or stomach. Forexample, based on the selected biomarker sensing systems data, mouth andesophagus-related biomarkers, complications, and/or contextualinformation, may be determined, including stomach tissue properties,esophageal motility, colonic tissue change, bacteria presence, tumorsize, tumor location, and/or tumor tension. Based on the selectedbiomarker sensing systems data, mouth and esophagus-related conditionsmay be predicted, including inflammation, surgical site infection (SSI),and/or gastro-esophageal disease. The mouth and esophagus may includemucosa, muscularis, lumen, and/or mechanical properties. Lumenbiomarkers may include lumen contents, lumen microbial flora, and/orlumen size. In an example, inflammation may be predicted based onanalyzed coughing biomarkers. Gastro-esophageal reflux disease may bepredicted based on inflammation. Stomach tissue properties may bepredicted based on gastro-esophageal disease. In an example, esophagealmotility may be determined based on collagen content and/or muscularisfunction. In an example, changes to colonic tissue may be indicatedbased on salivary cytokines. Inflammatory bowel disease (IBD) may bepredicted based on changes to colonic tissue. Salivary cytokines mayincrease in IBD. SSI may be predicted based on analyzed bacteria. Basedon the analyzed bacteria, the bacteria may be identified. Respiratorypathogens in the mouth may indicate likelihood of SSI. Based on lumensize and/or location, surgical tool parameter adjustments may begenerated. Surgical tool parameter adjustments may include staplesizing, surgical tool fixation, and/or surgical tool approach. In anexample, based on mechanical properties, including elasticity, asurgical tool parameter adjustment to use adjunct material may begenerated to minimize tissue tension. Additional mobilization parameteradjustments may be generated to minimize tissue tension based onanalyzed mechanical properties.

For example, based on the selected biomarker sensing systems data,stomach-related biomarkers, complications, and/or contextualinformation, may be determined including tissue strength, tissuethickness, recovery rate, lumen location, lumen shape, pancreasfunction, stomach food presence, stomach water content, stomach tissuethickness, stomach tissue shear strength, and/or stomach tissueelasticity. Based on the selected biomarker sensing systems data,stomach-related conditions may be predicted, including ulcer,inflammation, and/or gastro-esophageal reflux disease. The stomach mayinclude mucosa, muscularis, serosa, lumen, and mechanical properties.Stomach-related conditions, including ulcers, inflammation, and/orgastro-esophageal disease may be predicted based on analyzed coughingand/or GI tract imaging. Stomach tissue properties may be determinedbased on gastro-esophageal reflux disease. Ulcers may be predicted basedon analyzed H. pylori. Stomach tissue mechanical properties may bedetermined based on GT tract images. Surgical tool parameter adjustmentsmay be generated based on the determined stomach tissue mechanicalproperties. Risk of post-operative leak may be predicted based ondetermined stomach tissue mechanical properties. In an example, keycomponents for tissue strength and/or thickness may be determined basedon analyzed collagen content. Key components of tissue strength andthickness may affect recovery. In an example, blood supply and/or bloodlocation may be determined based on serosa biomarkers. In an example,biomarkers, including pouch size, pouch volume, pouch location, pancreasfunction, and/or food presence may be determined based on analyzed lumenbiomarkers. Lumen biomarkers may include lumen location, lumen shape,gastric emptying speed, and/or lumen contents. Pouch size may bedetermined based on start and end locations of the pouch. Gastricemptying speed may be determined based on GI motility. Pancreas functionmay be determined based on gastric emptying speed. Lumen content may bedetermined based on analyzed gastric pH. Lumen content may includestomach food presence. For example, solid food presence may bedetermined based on gastric pH variation. Low gastric pH may bepredicted based on an empty stomach. Basic gastric pH may be determinedbased on eating. Buffering by food may lead to basic gastric pH. GastricpH may increase based on stomach acid secretion. Gastric pH may returnto low value when the buffering capacity of food is exceeded.Intraluminal pH sensors may detect eating. For example, stomach watercontent, tissue thickness, tissue shear strength, and/or tissueelasticity may be determined based on tissue perfusion pressure. Stomachmechanical properties may be determined based on stomach water content.Surgical tool parameter adjustments may be generated based on thestomach mechanical properties. Surgical tool parameter adjustments maybe generated based on key components of tissue strength and/orfriability. Post-surgery leakage may be predicted based on keycomponents of tissue strength and/or friability.

The lower GI may include the small intestine, colon, and/or rectum. Forexample, based on the selected biomarker sensing systems data, smallintestine-related biomarkers, complications, contextual information,and/or conditions may be determined, including caloric absorption rate,nutrient absorption rate, bacteria presence, and/or recovery rate. Basedon the selected biomarker sensing systems data, small intestine-relatedconditions may be predicted, including ileus and/or inflammation. Thesmall intestine biomarkers may include muscularis, serosa, lumen,mucosa, and/or mechanical properties. For example, post-operation smallbowel motility changes may be determined based on GI motility. Ileus maybe predicted based on post-operation small bowel motility changes. GImotility may determine caloric and/or nutrient absorption rates. Futureweight loss may be predicted based on accelerated absorption rates.Absorption rates may be determined based on fecal rates, composition,and/or pH. Inflammation may be predicted based on lumen contentbiomarkers. Lumen content biomarkers may include pH, bacteria presence,and/or bacteria amount. Mechanical properties may be determined based onpredicted inflammation. Mucosa inflammation may be predicted based onstool inflammatory markers. Stool inflammatory markers may includecalprotectin. Tissue property changes may be determined based on mucosainflammation. Recovery rate changes may be determined based on mucosainflammation.

For example, based on the selected biomarker sensing systems data, colonand rectum-related biomarkers, complications, and/or contextualinformation may be determined, including small intestine tissuestrength, small intestine tissue thickness, contraction ability, watercontent, colon and rectum tissue perfusion pressure, colon and rectumtissue thickness, colon and rectum tissue strength, and/or colon andrectum tissue friability. Based on the selected biomarker sensingsystems data, colon and rectum-related conditions may be predicted,including inflammation, anastomotic leak, ulcerative colitis, Crohn'sdisease, and/or infection. Colon and rectum may include mucosa,muscularis, serosa, lumen, function, and/or mechanical properties. In anexample, mucosa inflammation may be predicted based on stoolinflammatory markers. Stool inflammatory markers may includecalprotectin. An increase in anastomotic leak risk may be determinedbased on inflammation.

Surgical tool parameter adjustments may be generated based on thedetermined increased risk of anastomotic leak. Inflammatory conditionsmay be predicted based on GI tract imaging. Inflammatory conditions mayinclude ulcerative colitis and/or Crohn's disease. Inflammation mayincrease the risk of anastomotic leak. Surgical tool parameteradjustments may be generated based on inflammation. In an example, thekey components of tissue strength and/or thickness may be determinedbased on collagen content. In an example, colon contraction ability maybe determined based on smooth muscle alpha-actin expression. In anexample, the inability of colon areas to contract may be determinedbased on abnormal expression. Colon contraction inability may bedetermined and/or predicted based on pseudo-obstruction and/or ileus. Inan example, adhesions, fistula, and/or scar tissue may be predictedbased on serosa biomarkers. Colon infection may be predicted based onbacterial presence in stool. The stool bacteria may be identified. Thebacteria may include commensals and/or pathogens. In an example,inflammatory conditions may be predicted based on pH. Mechanicalproperties may be determined based on inflammatory conditions. Gutinflammation may be predicted based on ingested allergens. Constantexposure, to ingested allergens may increase gut inflammation. Gutinflammation may change mechanical properties. In an example, mechanicalproperties may be determined based on tissue perfusion pressure. Watercontent may be determined based on tissue perfusion pressure. Surgicaltool parameter adjustments may be generated based on determinedmechanical properties.

Ancillary organs may include the pancreas, liver, spleen, and/orgallbladder. Based on the selected biomarker sensing systems data,ancillary organ-related biomarkers, complications, and/or contextualinformation may be determined including gastric emptying speed, liversize, liver shape, liver location, tissue health, and/or blood lossresponse. Based on the selected biomarker sensing systems data,ancillary organ-related conditions may be predicted, includinggastroparesis. For example, gastric emptying speed may be determinedbased on enzyme load and/or titratable base biomarkers. Gastroparesismay be predicted based on gastric emptying speed. Lymphatic tissuehealth may be determined based on lymphocyte storage status. A patient'sability to respond to an SSI may be determined based on lymphatic tissuehealth. Venous sinuses tissue health may be determined based on redblood cell storage status. A patient's response to blood loss in surgerymay be predicted based on venous sinuses tissue health.

Nutritional states may include short-term nutrition, long-termnutrition, and/or systemic nutrition. Based on the selected biomarkersensing systems data, nutritional state-related biomarkers,complications, and/or contextual information may be determined,including immune function. Based on the selected biomarker sensingsystems data, nutritional state-related conditions may be predicted,including cardiac issues. Reduced immune function may be determinedbased on nutrient biomarkers. Cardiac issues may be predicted based onnutrient biomarkers. Nutrient biomarkers may include macronutrients,micronutrients, alcohol consumption, and/or feeding patterns.

Patients who have had gastric bypass may have an altered gut microbiomethat may be measured in the feces.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data, and/or related biomarkers generated by the biomarkersensing systems.

The respiratory system may include the upper respiratory tract, lowerrespiratory tract, respiratory muscles, and/or system contents. Theupper respiratory tract may include the pharynx, larynx, mouth and oralcavity, and/or nose. The lower respiratory tract may include thetrachea, bronchi, aveoli, and/or lungs. The respiratory muscles mayinclude the diaphragm and/or intercostal muscles. Respiratorysystem-related biomarkers, complications, contextual information, and/orconditions may be determined and/or predicted based on analyzedbiomarker sensing systems data. A computing system, as described herein,may select one or more biomarkers (e.g., data from biomarker sensingsystems) from respiratory system-related biomarkers, including bacteria,coughing and sneezing, respiration rate, VO2 max, and/or activity foranalysis.

The upper respiratory tract may include the pharynx, larynx, mouth andoral cavity, and/or nose. For example, based on the selected biomarkersensing systems data, upper respiratory tract-related biomarkers,complications, and/or contextual information may be determined. Based onthe selected biomarker sensing systems data, upper respiratorytract-related conditions may be predicted, including SSI, inflammation,and/or allergic rhinitis. In an example, SSI may be predicted based onbacteria and/or tissue biomarkers. Bacteria biomarkers may include,commensals and/or pathogens. Inflammation may be indicated based ontissue biomarkers. Mucosa inflammation may be predicted based on nosebiomarkers, including coughing and sneezing. General inflammation and/orallergic rhinitis may be predicted based on mucosa biomarkers.Mechanical properties of various tissues may be determined based onsystemic inflammation.

The lower respiratory tract may include the trachea, bronchi, aveoli,and/or lungs. For example, based on the selected biomarker sensingsystems data, lower respiratory tract-related biomarkers, complications,and/or contextual information may be determined, includingbronchopulmonary segments. Based on the selected biomarker sensingsystems data, lower respiratory tract-related conditions may bepredicted. Surgical tool parameter adjustments may be generated based onthe determined biomarkers, complications, and/or contextual information.Surgical tool parameter adjustments may be generated based on thepredicted conditions.

Based on the selected biomarker sensing systems data, lung-relatedbiomarkers, complications, and/or contextual information may bedetermined, including poor surgical tolerance. Lung-related biomarkersmay include lung respiratory mechanics, lung disease, lung surgery, lungmechanical properties, and/or lung function. Lung respiratory mechanicsmay include total lung capacity (TLC), tidal volume (TV), residualvolume (RV), expiratory reserve volume (ERV), inspiratory reserve volume(IRV), inspiratory capacity (IC), inspiratory vital capacity (IVC),vital capacity (VC), functional residual capacity (FRC), residual volumeexpressed as a percent of total lung capacity (RV/TLC %), alveolar gasvolume (VA), lung volume (VL), forced vital capacity (FVC), forcedexpiratory volume over time (FEVt), difference between inspired andexpired carbon monoxide (DLco), volume exhaled after first second offorced expiration (FEV1), forced expiratory flow related to portion offunctional residual capacity curve (FEFx), maximum instantaneous flowduring functional residual capacity (FEFmax), forced inspiratory flow(FIF), highest forced expiratory flow measured by peak flow meter (PEF),and maximal voluntary ventilation (MVV).

TLC may be determined based on lung volume at maximal inflation. TV maybe determined based on volume of air moved into or out of the lungsduring quiet breathing. RV may be determined based on air volumeremaining in lungs after a maximal exhalation. ERV may be determinedbased on maximal volume inhaled from the end-inspiratory level. IC maybe determined based on aggregated IRV and TV values. IVC may bedetermined based on maximum air volume inhaled at the point of maximumexpiration. VC may be determined based on the difference between the RVvalue and TLC value. FRC may be determined based on the lung volume atthe end-expiratory position. FVC may be determined based on the VC valueduring a maximally forced expiratory effort. Poor surgical tolerance maybe determined based on the difference between inspired and expiredcarbon monoxide, such as when the difference falls below 60%. Poorsurgical tolerance may be determined based on the volume exhaled at theend of the first second of force expiration, such as when the volumefalls below 35%. MVV may be determined based on the volume of airexpired in a specified period during repetitive maximal effort.

Based on the selected biomarker sensing systems data, lung-relatedconditions may be predicted, including emphysema, chronic obstructivepulmonary disease, chronic bronchitis, asthma, cancer, and/ortuberculosis. Lung diseases may be predicted based on analyzedspirometry, x-rays, blood gas, and/or diffusion capacity of the aveolarcapillary membrane. Lung diseases may narrow airways and/or createairway resistance. Lung cancer and/or tuberculosis may be detected basedon lung-related biomarkers, including persistent coughing, coughingblood, shortness of breath, chest pain, hoarseness, unintentional weightloss, bone pain, and/or headaches. Tuberculosis may be predicted basedon lung symptoms including coughing for 3 to 5 weeks, coughing blood,chest pain, pain while breathing or coughing, unintentional weight loss,fatigue, fever, night sweats, chills, and/or loss of appetite.

Surgical tool parameter adjustments and surgical procedure adjustmentsmay be generated based on lung-related biomarkers, complications,contextual information, and/or conditions. Surgical procedureadjustments may include pneumonectomy, lobectomy, and/or sub-localresections. In an example, a surgical procedure adjustment may begenerated based on a cost-benefit analysis between adequate resectionand the physiologic impact on a patient's ability to recover functionalstatus. Surgical tool parameter adjustments may be generated based ondetermined surgical tolerance. Surgical tolerance may be determinedbased on the FEC1 value. Surgical tolerance may be considered adequatewhen FEV1 exceeds a certain threshold, which may include values above35%. Post-operation surgical procedure adjustments, includingoxygenation and/or physical therapy, may be generated based ondetermined pain scores. Post-operation surgical procedure adjustmentsmay be generated based on air leak. Air leak may increase costassociated with the post-surgical recovery and morbidity following lungsurgery.

Lung mechanical property-related biomarkers may include perfusion,tissue integrity, and/or collagen content. Plura perfusion pressure maybe determined based on lung water content levels. Mechanical propertiesof tissue may be determined based on plura perfusion pressure. Surgicaltool parameter adjustments may be generated based on plura perfusionpressure. Lung tissue integrity may be determined based on elasticity,hydrogen peroxide (H2O2) in exhaled breath, lung tissue thickness,and/or lung tissue shear strength. Tissue friability may be determinedbased on elasticity. Surgical tool parameter adjustments may begenerated based on post-surgery leakage. Post-surgery leakage may bepredicted based on elasticity. In an example, fibrosis may be predictedbased on H2O2 in exhaled breath. Fibrosis may be determined and/orpredicted based on increased H2O2 concentration. Surgical tool parameteradjustments may be generated based on predicted fibrosis. Increasedscarring in lung tissue may be determined based on predicted fibrosis.Surgical tool parameter adjustments may be generated based on determinedlung tissue strength. Lung tissue strength may be determined based onlung thickness and/or lung tissue shear strength. Post-surgery leakagemay be predicted based on lung tissue strength.

Respiratory muscles may include the diaphragm and/or intercostalmuscles. Based on the selected biomarker sensing systems data,respiratory muscle-related biomarkers, complications, and/or contextualinformation may be determined. Based on the selected biomarker sensingsystems data, respiratory muscle-related conditions may be predicted,including respiratory tract infections, collapsed lung, pulmonary edema,post-operation pain, air leak, and/or serious lung in frammationRespiratory muscle related conditions, including respiratory tractinfections, collapsed lung, and/or pulmonary edema, may be predictedbased on diaphragm-related biomarkers, including coughing and/orsneezing. Respiratory muscle-related conditions, includingpost-operation pain, air leak, collapsed lung, and/or serious lunginflammation may be predicted based on intercostal muscle biomarkers,including respiratory rate.

Based on the selected biomarker sensing systems data, respiratory systemcontent-related biomarkers, complications, and/or contextual informationmay be determined, including post-operation pain, healing ability,and/or response to surgical injury. Based on the selected biomarkersensing systems data, respiratory system content-related conditions maybe predicted, including inflammation and/or fibrosis. The selectedbiomarker sensing systems data may include environmental data, includingmycotoxins and/or airborne chemicals. Respiratory system content-relatedconditions may be predicted based on airborne chemicals. Inflammationand/or fibrosis may be predicted based on irritants in the environment.Mechanical properties of tissue may be determined based on inflammationand/or fibrosis. Post-operation pain may be determined based onirritants in the environment. Airway inflammation may be predicted basedon analyzed mycotoxins and/or arsenic. Surgical tool parameteradjustments may be generated based on airway inflammation. Alteredtissue properties may be determined based on analyzed arsenic.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing system, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

The endocrine system may include the hypothalamus, pituitary gland,thymus, adrenal gland, pancreas, testes, intestines, ovaries, thyroidgland, parathyroid, and/or stomach. Endocrine system-related biomarkers,complications, and/or contextual information may be determined based onanalyzed biomarker sensing systems data, including immune systemfunction, metastasis, infection risk, insulin secretion, collagenproduction, menstrual phase, and/or high blood pressure. Endocrinesystem-related conditions may be predicted based on analyzed biomarkersensing systems data. A computing system, as described herein, mayselect one or more biomarkers (e.g., data from biomarker sensingsystems) from endocrine system-related biomarkers, including hormones,blood pressure, adrenaline, cortisol, blood glucose, and/or menstrualcycle for analysis. Surgical tool parameter adjustments and/or surgicalprocedure adjustments may be generated based on the endocrinesystem-related biomarkers, complications, contextual information, and/orconditions.

For example, based on the selected biomarker sensing systems data,hypothalamus-related biomarkers, complications, and/or contextualinformation may be determined, including blood pressure regulation,kidney function, osmotic balance, pituitary gland control, and/or paintolerance. Based on the selected biomarker sensing systems data,hypothalamus-related conditions may be predicted, including edema. Thehormone biomarkers may include anti-diuretic hormone (ADH) and/oroxytocin. ADH may affect blood pressure regulation, kidney function,osmotic balance, and/or pituitary gland control. Pain tolerance may bedetermined based on analyzed oxytocin. Oxytocin may have an analgesiceffect. Surgical tool parameter adjustments may be generated based onpredicted edema.

For example, based on the selected biomarker sensing systems data,pituitary gland-related biomarkers, complications, and/or contextualinformation may be determined, including circadian rhythm entrainment,menstrual phase, and/or healing speed. Based on the selected biomarkersensing systems data, pituitary gland-related conditions may bepredicted. Circadian entrainment may be determined based onadrenocorticotropic hormones (ACTH). Circadian rhythm entrainment mayprovide context for various surgical outcomes. Menstrual phase may bedetermined based on reproduction function hormone biomarkers.Reproduction function hormone biomarkers may include luteinizing hormoneand/or follicle stimulating hormone. Menstrual phase may provide contextfor various surgical outcomes. The menstrual cycle may provide contextfor biomarkers, complications, and/or conditions, including thoserelated to the reproductive system. Wound healing speed may bedetermined based on thyroid regulation hormones, including thyrotropicreleasing hormone (TRH).

For example, based on the selected biomarker sensing systems data,thymus-related biomarkers, complications, and/or contextual informationmay be determined, including immune system function. Based on theselected biomarker sensing systems data, thymus-related conditions maybe predicted. Immune system function may be determined based onthymosins. Thymosins may affect adaptive immunity development.

For example, based on the selected biomarker sensing systems data,adrenal gland-related biomarkers, complications, and/or contextualinformation may be determined, including metastasis, blood vesselhealth, immunity level, and/or infection risk. Based on the selectedbiomarker sensing system data, adrenal gland-related conditions may bepredicted, including edema. Metastasis may be determined based onanalyzed adrenaline and/or nonadrenaline. Blood vessel health may bedetermined based on analyzed adrenaline and/or nonadrenaline. A bloodvessel health score may be generated based on the determined bloodvessel health. Immunity capability may be determined based on analyzedcortisol. Infection risk may be determined based on analyzed cortisol.Metastasis may be predicted based on analyzed cortisol. Circadian rhythmmay be determined based on measured cortisol. High cortisol may lowerimmunity, increase infection risk, and/or lead to metastasis. Highcortisol may affect circadian rhythm. Edema may be predicted based onanalyzed aldosterone. Aldosterone may promote fluid retention. Fluidretention may relate to blood pressure and/or edema.

For example, based on the selected biomarker sensing systems data,pancreas-related biomarkers, complications, and/or contextualinformation may be determined, including blood sugar, hormones,polypeptides, and/or blood glucose control. Based on the selectedbiomarker sensing systems data, pancreas-related conditions may bepredicted. The pancreas-related biomarkers may provide contextualinformation for various surgical outcomes. Blood sugar biomarkers mayinclude insulin. Hormone biomarkers may include somatostatin.Polypeptide biomarkers may include pancreatic polypeptide. Blood glucosecontrol may be determined based on insulin, somatostatin, and/orpancreatic polypeptide. Blood glucose control may provide contextualinformation for various surgical outcomes.

For example, based on the selected biomarker sensing systems data,testes-related biomarkers, complications, and/or contextual informationmay be determined, including reproductive development, sexual arousal,and/or immune system regulation. Based on the selected biomarker sensingsystems data, testes-related conditions may be predicted. Testes-relatedbiomarkers may include testosterone. Testosterone may provide contextualinformation for biomarkers, complications, and/or conditions, includingthose relating to the reproductive system. High levels of testosteronemay suppress immunity.

For example, based on the selected biomarker sensing systems data,stomach/testes-related biomarkers, complications, and/or contextualinformation may be determined, including glucose handling, satiety,insulin secretion, digestion speed, and/or sleeve gastrectomy outcomes.Glucose handling and satiety biomarkers may include glucagon-likepeptide-1 (GLP-1), cholecystokinin (CCK), and/or peptide YY. Appetiteand/or insulin secretion may be determined based on analyzed GLP-1.Increased GLP-1 may be determined based on enhanced appetite and insulinsecretion. Sleeve gastrectomy outcomes may be determined based onanalyzed GLP-1. Satiety and/or sleeve gastrectomy outcomes may bedetermined based on analyzed CCK. Enhanced CCK levels may be predictedbased on previous sleeve gastrectomy. Appetite and digestion speeds maybe determined based on analyzed peptide YY. Increased peptide YY mayreduce appetite and/or increase digestion speeds.

For example, based on the selected biomarker sensing systems data,hormone-related biomarkers, complications, and/or contextual informationmay be determined, including estrogen, progesterone, collagen product,fluid retention, and/or menstrual phase. Collagen production may bedetermined based on estrogen. Fluid retention may be determined based onestrogen. Surgical tool parameter adjustments may be generated based ondetermined collagen production and fluid retention.

For example, based on the selected biomarker sensing systems data,thyroid gland and parathyroid-related biomarkers, complications, and/orcontextual information may be determined, including calcium handling,phosphate handling, metabolism, blood pressure, and/or surgicalcomplications. Metabolism biomarkers may include triiodothyronine (T3)and/or thyroxine (T4). Blood pressure may be determined based onanalyzed T3 and T4. High blood pressure may be determined based onincreased T3 and/or increased T4. Surgical complications may bedetermined based on analyzed T3 and/or T4.

For example, based on the selected biomarker sensing systems data,stomach-related biomarkers, complications, and/or contextual informationmay be determined, including appetite. Stomach-related biomarkers mayinclude ghrelin. Ghrelin may induce appetite.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing system, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

Immune system-related biomarkers may relate to antigens and irritants,antimicrobial enzymes, the complement system, chemokines and cytokines,the lymphatic system, bone marrow, pathogens, damage-associatedmolecular patterns (DAMPs), and/or cells. Immune system-relatedbiomarkers, complications, and/or contextual information may bedetermined based on analyzed biomarker sensing systems data. A computingsystem, as described herein, may select one or more biomarkers (e.g.,data from biomarker sensing systems) from immune system-relatedbiomarkers, including alcohol consumption, pH, respiratory rate, edema,sweat, and/or environment for analysis.

For example, based on the selected biomarker sensing systems data,antigen and irritant-related biomarkers, complications, and/orcontextual information may be determined, including healing ability,immune function, and/or cardiac issues. Based on the selected biomarkersensing systems data, antigen and irritant-related conditions may bepredicted, including inflammation. Antigen and irritant-relatedbiomarkers may include inhaled chemicals, inhaled irritants, ingestedchemicals, and/or ingested irritants. Inhaled chemicals or irritants maybe determined based on analyzed environmental data, including airbornechemicals, mycotoxins, and/or arsenic. Airborne chemicals may includecigarette smoke, asbestos, crystalline silica, alloy particles, and/orcarbon nanotubes. Lung inflammation may be predicted based on analyzedairborne chemicals. Surgical tool parameter adjustments may be generatedbased on determined lung inflammation. Airway inflammation may bepredicted based on analyzed mycotoxin and/or arsenic. Surgical toolparameter adjustments may be generated based on determined airwayinflammation. Arsenic exposure may be determined based on urine, saliva,and/or ambient air sample analyses.

For example, based on the selected biomarker sensing systems data,antimicrobial enzyme-related biomarkers, complications, and/orcontextual information may be determined, including colon state. Basedon the selected biomarker sensing systems data, antimicrobialenzyme-related conditions may be predicted, including GI inflammation,acute kidney injury, E. faecalis infection, and/or S. aureus infection.Antimicrobial enzyme biomarkers may include lysozyme, lipocalin-2(NGAL), and/or orosomuccoid. GI inflammation may be predicted based onanalyzed lysozyme. Increased levels in lysozyme may be determined and/orpredicted based on GI inflammation. Colon state may be determined basedon analyzed lysozyme. Surgical tool parameter adjustments may begenerated based on analyzed lysozyme levels. Acute kidney injury may bepredicted based on analyzed NGAL. NGAL may be detected from serum and/orurine.

For example, based on the selected biomarker sensing systems data,complement system-related biomarkers, complications, and/or contextualinformation may be determined, including bacterial infectionsusceptibility. Bacterial infection susceptibility may be determinedbased on analyzed complement system deficiencies.

For example, based on the selected biomarker sensing systems data,chemokine and cytokine-related biomarkers, complications, and/orcontextual information may be determined, including infection burden,inflammation burden, vascular permeability regulation, omentin, colonictissue properties, and/or post-operation recovery. Based on the selectedbiomarker sensing systems data, chemokine and cytokine-relatedconditions may be predicted, including inflammatory bowel diseases,post-operation infection, lung fibrosis, lung scarring, pulmonaryfibrosis, gastroesophageal reflux disease, cardiovascular disease,edema, and/or hyperplasia. Infection and/or inflammation burdenbiomarkers may include oral, salivary, exhaled, and/or C-reactiveprotein (CRP) data. Salivary cytokines may include interleukin-1 beta(IL-1β), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α)and/or interleukin-8 (IL-8).

In an example, inflammatory bowel diseases may be predicted based onanalyzed salivary cytokines. Increased salivary cytokines may bedetermined based on inflammatory bowel diseases. Colonic tissueproperties may be determined based on predicted inflammatory boweldiseases. Colonic tissue properties may include scarring, edema, and/orulcering. Post-operation recovery and/or infection may be determinedbased on predicted inflammatory bowel diseases. Tumor size and/or lungscarring may be determined based on analyzed exhaled biomarkers. Lungfibrosis, pulmonary fibrosis, and/or gastroesophageal reflux disease maybe predicted based on analyzed exhaled biomarkers. Exhaled biomarkersmay include exhaled cytokines, pH, hydrogen peroxide (H2O2), and/ornitric oxide. Exhaled cytokines may include IL-6, TNF-α, and/orinterleukin-17 (IL-17). Lung fibrosis may be predicted based on measuredpH and/or H2O2 from exhaled. breath. Fibrosis may be predicted based onincreased H2O2 concentration. Increased lung tissue scarring may bepredicted based on fibrosis. Surgical tool parameter adjustments may begenerated based on predicted lung fibrosis. In an example, pulmonaryfibrosis and/or gastroesophageal reflux disease may be predicted basedon analyzed exhaled nitric oxide. Pulmonary fibrosis may be predictedbased on determined increased nitrates and/or nitrites. Gastroesophagealdisease may be predicted based on determined reduced nitrates and/ornitrites. Surgical tool parameter adjustments may be generated based onpredicted pulmonary fibrosis and/or gastroesophageal reflux disease.Cardiovascular disease, inflammatory bowel diseases, and/or infectionmay be predicted based on analyzed CRP biomarkers. Risk of seriouscardiovascular disease may increase with high CRP concentration.Inflammatory bowel disease may be predicted based on elevated CRPconcentration. Infection may be predicted based on elevated CRPconcentration. In an example, edema may be predicted based on analyzedvascular permeability regulation biomarkers. Increased vascularpermeability during inflammation may be determined based on analyzedbradykinin and/or histamine. Edema may be predicted based on increasedvascular permeability during inflammation. Vascular permeability may bedetermined based on endothelial adhesion molecules. Endothelial adhesionmolecules may be determined based on cell samples. Endothelial adhesionmolecules may affect vascular permeability, immune cell recruitment,and/or fluid build-up in edema. Surgical tool parameter adjustments maybe generated based on analyzed vascular permeability regulationbiomarkers. In an example, hyperplasia may be predicted based onanalyzed omentin. Hyperplasia may alter tissue properties. Surgical toolparameter adjustments may be generated based on predicted hyperplasia.

For example, based on the selected biomarker sensing systems data,lymphatic system-related biomarkers, complications, and/or contextualinformation may be determined, including lymph nodes, lymph composition,lymph location, and/or lymph swelling. Based on the selected biomarkersensing systems data, lymphatic system related conditions may bepredicted, including post-operation inflammation, post-operationinfection, and/or fibrosis. Post-operation inflammation and/or infectionmay be predicted based on determined lymph node swelling. Surgical toolparameter adjustments may be generated based on the analyzed lymph nodeswelling. Surgical tool parameter adjustments, including harmonic toolparameter adjustments, may be generated based on the determined collagendeposition. Collagen deposition may increase with lymph node fibrosis.Inflammatory conditions may be predicted based on lymph composition.Metastatic cell spread may be determined based on lymph composition.Surgical tool parameter adjustments may be generated based on lymphpeptidome. Lymph peptidome may change based on inflammatory conditions.

For example, based on the selected biomarker sensing systems data,pathogen-related biomarkers, complications, and/or contextualinformation may be determined, including pathogen-associated molecularpatterns (PAMPs), pathogen burden, H. Pylori, and/or stomach tissueproperties. Based on the selected biomarker sensing systems data,pathogen-related conditions may be predicted, including infection,stomach inflammation, and/or ulcering. PAMPs biomarkers may includepathogen antigens. Pathogen antigens may impact pathogen burden. Stomachinflammation and/or potential ulcering may be predicted based onpredicted infection. Stomach tissue property alterations may bedetermined based on predicted infection.

For example, based on the selected biomarker sensing systems data,DAMPs-related biomarkers, complications, and/or contextual informationmay be determined, including stress (e.g., cardiovascular, metabolic,glycemic, and/or cellular) and/or necrosis. Based on the selectedbiomarker sensing systems data, DAMPs-related conditions may bepredicted, including acute myocardial infarction, intestinalinflammation, and/or infection. Cellular stress biomarkers may includecreatine kinase MB, pyruvate kinase isoenzyme type M2 (M2-PK), irisin,and/or microRNA. In an example, acute myocardial infarction may bepredicted based on analyzed creatine kinase MB biomarkers. Intestinalinflammation may be predicted based on analyzed M2-PK biomarkers. Stressmay be determined based on analyzed irisin biomarkers, inflammatorydiseases and/or infection may be predicted based on analyzed microRNAbiomarkers. Surgical tool parameter adjustments may be generated basedon predicted inflammation and/or infection. Inflammation and/orinfection may be predicted based on analyzed necrosis biomarkers.Necrosis biomarkers may include reactive oxygen species (ROS).Inflammation and/or infection may be predicted based on increased ROS.Post-operation recovery may be determined based on analyzed ROS.

For example, based on the selected biomarker sensing systems,cell-related biomarkers, complications, and/or contextual informationmay be determined, including granulocytes, natural killer cells (NKcells), macrophages, lymphocytes, and/or colonic tissue properties.Based on the selected biomarker sensing systems, cell-related conditionsmay be predicted, including post-operation infection, ulceratic colitis,inflammation, and/or inflammatory bowel disease. Granulocyte biomarkersmay include eosinophilia and/or neutrophils. Eosinophilia biomarkers mayinclude sputum cell count, eosinophilic cationic protein, and/orfractional exhaled nitric oxide. Neutrophil biomarkers may include S100proteins, myeloperoxidase, and/or human neutrophil lipocalin. Lymphocytebiomarkers may include antibodies, adaptive response, and/or immunememory. The antibodies may include immunoglobulin A (IgA) and/orimmunoglobulin M (IgM). In an example, post-operational infection and/orpre-operation inflammation may be predicted based on analyzed sputumcell count. Ulcerative colitis may be predicted based on analyzedeosinophilic cationic protein. Altered colonic tissue properties may bedetermined based on the predicted ulcerative colitis. Eosinophils mayproduce eosinophilic cationic protein which may be determined based onulcerative colitis. Inflammation may be predicted based on analyzedfractional exhaled nitric oxide. The inflammation may include type 1asthma-like inflammation. Surgical tool parameter adjustments may begenerated based on the predicted inflammation. In an example,inflammatory bowel diseases may be predicted based on S100 proteins. TheS100 proteins may include calprotectin. Colonic tissue properties may bedetermined based on the predicted inflammatory bowel diseases.Ulcerative colitis may be predicted based on analyzed myeloperoxidaseand/or human neutrophil lipocalin. Altered colonic tissue properties maybe determined based on predicted ulcerative colitis. In an example,inflammation may be predicted based on antibody biomarkers. Bowelinflammation may be predicted based on IgA. Cardiovascular inflammationmay be predicted based on IgM.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

Tumors may include benign and/or malignant tumors. Tumor-relatedbiomarkers, complications, contextual information, and/or conditions maybe determined and/or predicted based on analyzed biomarker sensingsystems data. A computing system, as described herein, may select one ormore biomarkers (e.g., data from biomarker sensing systems) fromtumor-related biomarkers, including circulating tumor cells foranalysis.

For example, based on the selected biomarker sensing systems data,benign tumor-related biomarkers, conditions, and/or contextual information may be determined, including benign tumor replication, benigntumor metabolism, and/or benign tumor synthesis. Benign tumorreplication may include rate of mitotic activity, mitotic metabolism,and/or synthesis biomarkers. Benign tumor metabolism may includemetabolic demand and/or metabolic product biomarkers. Benign tumorsynthesis may include protein expression and/or gene expressionbiomarkers.

For example, based on the selected biomarker sensing systems data,malignant tumor-related biomarkers, complications, and/or contextualinformation may be determined, including malignant tumor synthesis,malignant tumor metabolism, malignant tumor replication, microsatellitestability, metastatic risk, metastatic tumors, tumor growth, tumorrecession, and/or metastatic activity. Based on the selected biomarkersensing systems data, malignant tumor-related conditions may bepredicted, including cancer. Malignant tumor synthesis may include geneexpression and/or protein expression biomarkers. Gene expression may bedetermined based on tumor biopsy and/or genome analysis. Proteinexpression biomarkers may include cancer antigen 125 (CA-125) and/orcarcinoembryonic antigen (CEA). CEA may be measured based on urineand/or saliva. Malignant tumor replication data may include rate ofmitotic activity, mitotic encapsulation, tumor mass, and/or microRNA200c.

In an example, microsatellite stability may be determined based onanalyzed gene expression. Metastatic risk may be determined based ondetermined microsatellite stability. Higher metastatic risk may bedetermined and/or predicted based on low microsatellite. In an example,metastatic tumors, tumor growth, tumor metastasis, and/or tumorrecession may be determined based on analyzed protein expression.Metastatic tumors may be determined and/or predicted based on elevatedCA-125. Cancer may be predicted based CA-125. Cancer may be predictedbased on certain levels of CEA. Tumor growth, metastasis, and/orrecession may be monitored based on detected changes in CEA. Metastaticactivity may be determined based on malignant tumor replication. Cancermay be predicted based on malignant tumor replication. MicroRNA 200c maybe released into blood by certain cancers. Metastatic activity may bedetermined and/or predicted based on presence of circulating tumorcells.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

The musculoskeletal system may include muscles, bones, marrow, and/orcartilage. The muscles may include smooth muscle, cardiac muscle, and/orskeletal muscle. The smooth muscle may include calmodulin, connectivetissue, structural features, hyperplasia, actin, and/or myosin. Thebones may include calcified bone, osteoblasts, and/or osteoclasts. Themarrow may include red marrow and/or yellow marrow. The cartilage mayinclude cartilaginous tissue and/or chondrocytes. Musculoskeletalsystem-related biomarkers complications, contextual information, and/orconditions may be determined and, predicted based on analyzed biomarkersensing systems data. A computing system, as described herein, mayselect one or more biomarkers (e.g., data from biomarker sensingsystems) from musculoskeletal-related biomarkers for analysis.

For example, based on the selected biomarker sensing systems data,muscle-related biomarkers, complications, and/or contextual informationmay be determined, including serum calmodulin levels, mechanicalstrength, muscle body, hyperplasia, muscle contraction ability, and/ormuscle damage. Based on the selected biomarker sensing systems data,muscle-related conditions may be predicted. In an example, neurologicalconditions may be predicted based on analyzed serum calmodulin levels.Mechanical strength may be determined based on analyzed smooth musclecollagen levels. Collagen may affect mechanical strength as collagen maybind smooth muscle filament together. Muscle body may be determinedbased on analyzed structural features. The muscle body may include anintermediate body and/or a dense body. Hyperplasia may be determinedbased on analyzed omentin levels. Omentin may indicate hyperplasia.Hyperplasia may be determined and/or predicted based on thick areas ofsmooth muscles. Muscle contraction ability may be determined based onanalyzed smooth muscle alpha-actin expression. Muscle contractioninability may result from an abnormal expression of actin in smoothmuscle. In an example, muscle damage may be determined based on analyzedcirculating smooth muscle myosin and/or skeletal muscle myosin. Musclestrength may be determined based on analyzed circulating smooth musclemyosin. Muscle damage and/or weak, friable smooth muscle may bedetermined and/or predicted based on circulating smooth muscle myosinand/or skeletal muscle myosin. Smooth muscle myosin may be measured fromurine. In an example, muscle damage may be determined based on cardiacand/or skeletal muscle biomarkers. Cardiac and/or skeletal musclebiomarkers may include circulating troponin. Muscle damage may bedetermined and/or predicted based on circulating troponin alongsidemyosin.

For example, based on the selected biomarker sensing systems data,bone-related biomarkers, complications, and/or contextual informationmay be determined, including calcified bone properties, calcified bonefunctions, osteoblasts number, osteoid secretion, osteoclasts number,and/or secreted osteoclasts.

For example, based on the selected biomarker sensing systems data,marrow-related biomarkers, complications, and/or contextual informationmay be determined, including tissue breakdown and/or collagen secretion.Arthritic breakdown of cartilaginous tissue may be determined based onanalyzed cartilaginous tissue biomarkers. Collage secretion by musclecells may be determined based on analyzed chondrocyte biomarkers.

The detection, prediction, determination, and/or generation describedherein may be performed by a computing system described herein, such asa surgical hub, a computing device, and/or a smart device based onmeasured data and/or related biomarkers generated by the biomarkersensing systems.

Reproductive system-related biomarkers, complications, contextualinformation, and/or conditions may be determined and/or predicted basedon analyzed biomarker sensing systems data. A computing system, asdescribed herein, may select one or more biomarkers (e.g., data frombiomarker sensing systems) from reproductive system-related biomarkersfor analysis. Reproductive system-related biomarkers, complications,and/or contextual information may be determined based on analyzedbiomarker sensing systems data, including female anatomy, femalefunction, menstrual cycle, pH, bleeding, wound healing, and/or scarring.Female anatomy biomarkers may include the ovaries, vagina, cervix,fallopian tubes, and/or uterus. Female function biomarkers may includereproductive hormones, pregnancy, menopause, and/or menstrual cycle.Reproductive system-related conditions may be predicted based onanalyzed biomarker sensing systems data, including endometriosis,adhesions, vaginosis, bacterial infection, SSI, and/or pelvic abscesses.

In an example, endometriosis may be predicted based on female anatomybiomarkers. Adhesions may be predicted based on female anatomybiomarkers. The adhesions may include sigmoid colon adhesions.Endometriosis may be predicted based on menstrual blood. Menstrual bloodmay include molecular signals from endometriosis. Sigmoid colonadhesions may be predicted based on predicted endometriosis. In anexample, menstrual phase and/or menstrual cycle length may be determinedbased on the menstrual cycle. Bleeding, wound healing, and/or scarringmay be determined based on the analyzed menstrual phase. Risk ofendometriosis may be predicted based on the analyzed menstrual cycle.Higher risk of endometriosis may be predicted based on shorter menstrualcycle lengths. Molecular signals may be determined based on analyzedmenstrual blood and/or discharge pH. Endometriosis may be predictedbased on the determined molecular signals. Vaginal pH may be determinedbased on analyzed discharge pH. Vaginosis and/or bacterial infectionsmay be predicted based on the analyzed vaginal pH. Vaginosis and/orbacterial infections may be predicted based on changes in vaginal pH.Risk of SSI and/or pelvic abscesses during gynecologic procedures may bepredicted based on predicted vaginosis.

The detection, prediction, determination, and/or generation describedherein may be performed by any of the computing systems within any ofthe computer-implemented patient and surgeon monitoring systemsdescribed herein, such as a surgical hub, a computing device, and/or asmart device based on measured data and/or related biomarkers generatedby the one or more sensing systems.

FIG. 2A shows an example of a surgeon monitoring system 20002 in asurgical operating room. As illustrated in FIG. 2A, a patient is beingoperated on by one or more health care professionals (HCPs). The HCPsare being monitored by one or more surgeon sensing systems 20020 worn bythe HCPs. The HCPs and the environment surrounding the HCPs may also bemonitored by one or more environmental sensing systems including, forexample, a set of cameras 20021, a set of microphones 20022, and othersensors, etc. that may be deployed in the operating room. The surgeonsensing systems 20020 and the environmental sensing systems may be incommunication with a surgical hub 20006, which in turn may be incommunication with one or more cloud servers 20009 of the cloudcomputing system 20008, as shown in FIG. 1. The environmental sensingsystems may be used for measuring one or more environmental attributes,for example, HCP position in the surgical theater, HCP movements,ambient noise in the surgical theater, temperature/humidity in thesurgical theater, etc.

As illustrated in FIG. 2A, a primary display 20023 and one or more audiooutput devices (e.g., speakers 20019) are positioned in the sterilefield to be visible to an operator at the operating table 20024. Inaddition, a visualization/notification tower 20026 is positioned outsidethe sterile field. The visualization/notification tower 20026 mayinclude a first non-sterile human interactive device (HID) 20027 and asecond non-sterile HID 20029, which may face away from each other. TheHID may be a display or a display with a touchscreen allowing a human tointerface directly with the HID. A human interface system, guided by thesurgical hub 20006, may be configured to utilize the HIDs 20027, 20029,and 20023 to coordinate information flow to operators inside and outsidethe sterile field. In an example, the surgical hub 20006 may cause anHID (e.g., the primary HID 20023) to display a notification and/orinformation about the patient and/or a surgical procedure step. In anexample, the surgical hub 20006 may prompt for and/or receive input frompersonnel in the sterile field or in the non-sterile area. In anexample, the surgical hub 20006 may cause an HID to display a snapshotof a surgical site, as recorded by an imaging device 20030, on anon-sterile HID 20027 or 20029, while maintaining a live feed of thesurgical site on the primary HID 20023. The snapshot on the non-steriledisplay 20027 or 20029 can permit a non-sterile operator to perform adiagnostic step relevant to the surgical procedure, for example.

In one aspect, the surgical hub 20006 may be configured to route adiagnostic input or feedback entered by a non-sterile operator at thevisualization tower 20026 to the primary display 20023 within thesterile field, where it can be viewed by a sterile operator at theoperating table. In one example, the input can be in the form of amodification to the snapshot displayed on the non-sterile display 20027or 20029, which can be routed to the primary display 20023 by thesurgical hub 20006.

Referring to FIG. 2A, a surgical instrument 20031 is being used in thesurgical procedure as part of the surgeon monitoring system 20002. Thehub 20006 may be configured to coordinate information flow to a displayof the surgical instrument 20031. For example, in U.S. PatentApplication Publication No. US 2019-0200844 A1 (U.S. patent applicationSer. No. 16/209,385), titled METHOD OF HUB COMMUNICATION, PROCESSING,STORAGE AND DISPLAY, filed Dec. 4, 2018, the disclosure of which isherein incorporated by reference in its entirety. A diagnostic input orfeedback entered by a non-sterile operator at the visualization tower20026 can be routed by the hub 20006 to the surgical instrument displaywithin the sterile field, where it can be viewed by the operator of thesurgical instrument 20031. Example surgical instruments that aresuitable for use with the surgical system 20002 are described under theheading “Surgical Instrument Hardware” and in U.S. Patent ApplicationPublication No. US 2019-0200844 A1 (U.S. patent application Ser. No.16/209,385), titled METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE ANDDISPLAY, filed Dec. 4, 2018, the disclosure of which is hereinincorporated by reference in its entirety, for example.

FIG. 2A illustrates an example of a surgical system 20002 being used toperform a surgical procedure on a patient who is lying down on anoperating table 20024 in a surgical operating room 20035. A roboticsystem 20034 may be used in the surgical procedure as a part of thesurgical system 20002. The robotic system 20034 may include a surgeon'sconsole 20036, a patient side cart 20032 (surgical robot), and asurgical robotic hub 20033. The patient side cart 20032 can manipulateat least one removably coupled surgical tool 20037 through a minimallyinvasive incision in the body of the patient while the surgeon views thesurgical site through the surgeon's console 20036. An image of thesurgical site can be obtained by a medical imaging device 20030, whichcan be manipulated by the patient side cart 20032 to orient the imagingdevice 20030. The robotic hub 20033 can be used to process the images ofthe surgical site for subsequent display to the surgeon through thesurgeon's console 20036.

Other types of robotic systems can be readily adapted for use with thesurgical system 20002. Various examples of robotic systems and surgicaltools that are suitable for use with the present disclosure aredescribed in U.S. Patent Application Publication No. US 2019-0201137 A1(U.S. patent application Ser. No. 16/209,407), titled METHOD OF ROBOTICHUB COMMUNICATION, DETECTION, AND CONTROL, filed Dec. 4, 2018, thedisclosure of which is herein incorporated by reference in its entirety.

Various examples of cloud-based analytics that are performed by thecloud computing system 20008, and are suitable for use with the presentdisclosure, are described in U.S. Patent Application Publication No. US2019-0206569 A1 (U.S. patent application Ser. No. 16/209,403), titledMETHOD OF CLOUD BASED DATA ANALYTICS FOR USE WITH THE HUB, filed Dec. 4,2018, the disclosure of which is herein incorporated by reference in itsentirety.

In various aspects, the imaging device 20030 may include at least oneimage sensor and one or more optical components. Suitable image sensorsmay include, but are not limited to, Charge-Coupled Device (CCD) sensorsand Complementary Metal-Oxide Semiconductor (CMOS) sensors.

The optical components of the imaging device 20030 may include one ormore illumination sources and/or one or more lenses. The one or moreillumination sources may be directed to illuminate portions of thesurgical field. The one or more image sensors may receive lightreflected or refracted from the surgical field, including lightreflected or refracted from tissue and/or surgical instruments.

The one or more illumination sources may be configured to radiateelectromagnetic energy in the visible spectrum as well as the invisiblespectrum. The visible spectrum, sometimes referred to as the opticalspectrum or luminous spectrum, is that portion of the electromagneticspectrum that is visible to (i.e., can be detected by) the human eye andmay be referred to as visible light or simply light. A typical human eyewill respond to wavelengths in air that range from about 380 nm to about750 nm.

The invisible spectrum (e.g., the non-luminous spectrum) is that portionof the electromagnetic spectrum that lies below and above the visiblespectrum (i.e., wavelengths below about 380 nm and above about 750 nm).The invisible spectrum is not detectable by the human eye. Wavelengthsgreater than about 750 nm are longer than the red visible spectrum, andthey become invisible infrared (IR), microwave, and radioelectromagnetic radiation. Wavelengths less than about 380 nm areshorter than the violet spectrum, and they become invisible ultraviolet,x-ray, and gamma ray electromagnetic radiation.

In various aspects, the imaging device 20030 is configured for use in aminimally invasive procedure. Examples of imaging devices suitable foruse with the present disclosure include, but are not limited to, anarthroscope, angioscope, bronchoscope, choledochoscope, colonoscope,otoscope, duodenoscope, enteroscope, esophagogastro-duodenoscope(gastroscope), endoscope, laryngoscope, nasopharyngo-neproscope,sigmoidoscope, thoracoscope, and ureteroscope.

The imaging device may employ multi-spectrum monitoring to discriminatetopography and underlying structures. A multi-spectral image is one thatcaptures image data within specific wavelength ranges across theelectromagnetic spectrum. The wavelengths may be separated by filters orby the use of instruments that are sensitive to particular wavelengths,including light from frequencies beyond the visible light range, e.g.,IR and ultraviolet. Spectral imaging can allow extraction of additionalinformation that the human eye fails to capture with its receptors forred, green, and blue. The use of multi-spectral imaging is described ingreater detail under the heading “Advanced Imaging Acquisition Module”in U.S. Patent Application Publication No. US 2019-0200844 A1 (U.S.patent application Ser. No. 16/209,385), titled METHOD OF HUBCOMMUNICATION, PROCESSING, STORAGE AND DISPLAY, filed Dec. 4, 2018, thedisclosure of which is herein incorporated by reference in its entirety.Multi-spectrum monitoring can be a useful tool in relocating a surgicalfield after a surgical task is completed to perform one or more of thepreviously described tests on the treated tissue. It is axiomatic thatstrict sterilization of the operating room and surgical equipment isrequired during any surgery. The strict hygiene and sterilizationconditions required in a “surgical theater,” i.e., an operating ortreatment room, necessitate the highest possible sterility of allmedical devices and equipment. Part of that sterilization process is theneed to sterilize anything that comes in contact with the patient orpenetrates the sterile field, including the imaging device 20030 and itsattachments and components. It will be appreciated that the sterilefield may be considered a specified area, such as within a tray or on asterile towel, that is considered free of microorganisms, or the sterilefield may be considered an area, immediately around a patient, who hasbeen prepared for a surgical procedure. The sterile field may includethe scrubbed team members, who are properly attired, and all furnitureand fixtures in the area.

Wearable sensing system 20011 illustrated in FIG. 1 may include one ormore sensing systems, for example, surgeon sensing systems 20020 asshown in FIG. 2A. The surgeon sensing systems 20020 may include sensingsystems to monitor and detect a set of physical states and/or a set ofphysiological states of a healthcare provider (HCP). An HCP may be asurgeon or one or more healthcare personnel assisting the surgeon orother healthcare service providers in general. In an example, a sensingsystem 20020 may measure a set of biomarkers to monitor the heart rateof an HCP. In another example, a sensing system 20020 worn on asurgeon's wrist (e.g., a watch or a wristband) may use an accelerometerto detect hand motion and/or shakes and determine the magnitude andfrequency of tremors. The sensing system 20020 may send the measurementdata associated with the set of biomarkers and the data associated witha physical state of the surgeon to the surgical hub 20006 for furtherprocessing. One or more environmental sensing devices may sendenvironmental information to the surgical hub 20006. For example, theenvironmental sensing devices may include a camera 20021 for detectinghand/body position of an HCP. The environmental sensing devices mayinclude microphones 20022 for measuring the ambient noise in thesurgical theater. Other environmental sensing devices may includedevices, for example, a thermometer to measure temperature and ahygrometer to measure humidity of the surroundings in the surgicaltheater, etc. The surgical hub 20006, alone or in communication with thecloud computing system, may use the surgeon biomarker measurement dataand/or environmental sensing information to modify the controlalgorithms of hand-held instruments or the averaging delay of a roboticinterface, for example, to minimize tremors. In an example, the surgeonsensing systems 20020 may measure one or more surgeon biomarkersassociated with an HCP and send the measurement data associated with thesurgeon biomarkers to the surgical hub 20006. The surgeon sensingsystems 20020 may use one or more of the following RF protocols forcommunicating with the surgical hub 20006: Bluetooth, BluetoothLow-Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low-powerwireless Personal Area Network (6LoWPAN), Wi-Fi. The surgeon biomarkersmay include one or more of the following stress, heart rate, etc. Theenvironmental measurements from the surgical theater may include ambientnoise level associated with the surgeon or the patient, surgeon and/orstaff movements, surgeon and/or staff attention level, etc.

The surgical hub 20006 may use the surgeon biomarker measurement dataassociated with an HCP to adaptively control one or more surgicalinstruments 20031. For example, the surgical hub 20006 may send acontrol program to a surgical instrument 20031 to control its actuatorsto limit or compensate for fatigue and use of fine motor skills. Thesurgical hub 20006 may send the control program based on situationalawareness and/or the context on importance or criticality of a task. Thecontrol program may instruct the instrument to alter operation toprovide more control when control is needed.

FIG. 2B shows an example of a patient monitoring system 20003 (e.g., acontrolled patient monitoring system). As illustrated in FIG. 2B, apatient in a controlled environment (e.g., in a hospital recovery room)may be monitored by a plurality of sensing systems (e.g., patientsensing systems 20041). A patient sensing system 20041 (e.g., a headband) may be used to measure an electroencephalogram (EEG) to measureelectrical activity of the brain of a patient. A patient sensing system20042 may be used to measure various biomarkers of the patientincluding, for example, heart rate, VO2 level, etc. A patient sensingsystem 20043 (e.g., flexible patch attached to the patient's skin) maybe used to measure sweat lactate and/or potassium levels by analyzingsmall amounts of sweat that is captured from the surface of the skinusing microfluidic channels. A patient sensing system 20044 (e.g., awristband or a watch) may be used to measure blood pressure, heart rate,heart rate variability, VO2 levels, etc. using various techniques, asdescribed herein. A patient sensing system 20045 (e.g., a ring onfinger) may be used to measure peripheral temperature, heart rate, heartrate variability, VO2 levels, etc. using various techniques, asdescribed herein. The patient sensing systems 20041-20045 may use aradio frequency (RF) link to be in communication with the surgical hub20006. The patient sensing systems 20041-20045 may use one or more ofthe following RF protocols for communication with the surgical hub20006: Bluetooth, Bluetooth Low-Energy (BLE), Bluetooth Smart, Zigbee,Z-wave, IPv6 Low-power wireless Personal Area Network (6LoWPAN), Thread,Wi-Fi, etc.

The sensing systems 20041-20045 may be in communication with a surgicalhub 20006, which in turn may be in communication with a remote server20009 of the remote cloud computing system 20008. The surgical hub 20006is also in communication with an HID 20046. The HID 20046 may displaymeasured data associated with one or more patient biomarkers. Forexample, the HID 20046 may display blood pressure, Oxygen saturationlevel, respiratory rate, etc. The HID 20046 may display notificationsfor the patient or an HCP providing information about the patient, forexample, information about a recovery milestone or a complication. In anexample, the information about a recovery milestone or a complicationmay be associated with a surgical procedure the patient may haveundergone. In an example, the HID 20046 may display instructions for thepatient to perform an activity. For example, the HID 20046 may displayinhaling and exhaling instructions. In an example the HID 20046 may bepart of a sensing system.

As illustrated in FIG. 2B, the patient and the environment surroundingthe patient may be monitored by one or more environmental sensingsystems 20015 including, for example, a microphone (e.g., for detectingambient noise associated with or around a patient), atemperature/humidity sensor, a camera for detecting breathing patternsof the patient, etc. The environmental sensing systems 20015 may be incommunication with the surgical hub 20006, which in turn is incommunication with a remote server 20009 of the remote cloud computingsystem 20008.

In an example, a patient sensing system 20044 may receive a notificationinformation from the surgical hub 20006 for displaying on a display unitor an HID of the patient sensing system 20044. The notificationinformation may include a notification about a recovery milestone or anotification about a complication, for example, in case of post-surgicalrecovery. In an example, the notification information may include anactionable severity level associated with the notification. The patientsensing system 20044 may display the notification and the actionableseverity level to the patient. The patient sensing system may alert thepatient using a haptic feedback. The visual notification and/or thehaptic notification may be accompanied by an audible notificationprompting the patient to pay attention to the visual notificationprovided on the display unit of the sensing system.

FIG. 2C shows an example of a patient monitoring system (e.g., anuncontrolled patient monitoring system 20004). As illustrated in FIG.2C, a patient in an uncontrolled environment (e.g., a patient'sresidence) is being monitored by a plurality of patient sensing systems20041-20045. The patient sensing systems 20041-20045 may measure and/ormonitor measurement data associated with one or more patient biomarkers.For example, a patient sensing system 20041, a head band, may be used tomeasure an electroencephalogram (EEG). Other patient sensing systems20042, 20043, 20044, and 20045 are examples where various patientbiomarkers are monitored, measured, and/or reported, as described inFIG. 2B. One or more of the patient sensing systems 20041-20045 may besend the measured data associated with the patient biomarkers beingmonitored to the computing device 20047, which in turn may be incommunication with a remote server 20009 of the remote cloud computingsystem 20008. The patient sensing systems 20041-20045 may use a radiofrequency (RF) link to be in communication with a computing device 20047(e.g., a smart phone, a tablet, etc). The patient sensing systems20041-20045 may use one or more of the following RF protocols forcommunication with the computing device 20047: Bluetooth, BluetoothLow-Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low-powerwireless Personal Area Network (6LoWPAN), Thread, Wi-Fi, etc. In anexample, the patient sensing systems 20041-20045 may be connected to thecomputing device 20047 via a wireless router, a wireless hub, or awireless bridge.

The computing device 20047 may be in communication with a remote server20009 that is part of a cloud computing system 20008. In an example, thecomputing device 20047 may be in communication with a remote server20009 via an internet service provider's cable/FIOS networking node. Inan example, a patient sensing system may be in direct communication witha remote server 20009. The computing device 20047 or the sensing systemmay communicate with the remote servers 20009 via a cellulartransmission/reception point (TRP) a base station using one or more ofthe following cellular protocols: GSM/GPRS/EDGE (2G), UMTS/HSPA (3G),long term evolution (LTE) or 4G, LTE-Advanced (LTE-A), new radio (NR) or5G.

In an example, a computing device 20047 may display informationassociated with a patient biomarker. For example, a computing device20047 may display blood pressure, Oxygen saturation level, respiratoryrate, etc. A computing device 20047 may display notifications for thepatient or an HCP providing information about the patient, for example,information about a recovery milestone or a complication.

In an example, the computing device 20047 and/or the patient sensingsystem 20044 may receive a notification information from the surgicalhub 20006 for displaying on a display unit of the computing device 20047and/or the patient sensing system 20044. The notification informationmay include a notification about a recovery milestone or a notificationabout a complication, for example, in case of post-surgical recovery.The notification information may also include an actionable severitylevel associated with the notification. The computing device 20047and/or the sensing system 20044 may display the notification and theactionable severity level to the patient. The patient sensing system mayalso alert the patient using a haptic feedback. The visual notificationand/or the haptic notification may be accompanied by an audiblenotification prompting the patient to pay attention to the visualnotification provided on the display unit of the sensing system.

FIG. 3 shows an example surgeon monitoring system 20002 with a surgicalhub 20006 paired with a wearable sensing system 20011, an environmentalsensing system 20015, a human interface system 20012, a robotic system20013, and an intelligent instrument 20014. The hub 20006 includes adisplay 20048, an imaging module 20049, a generator module 20050, acommunication module 20056, a processor module 20057, a storage array20058, and an operating-room mapping module 20059. In certain aspects,as illustrated in FIG. 3, the hub 20006 further includes a smokeevacuation module 20054 and/or a suction/irrigation module 20055. Duringa surgical procedure, energy application to tissue, for sealing and/orcutting, is generally associated with smoke evacuation, suction ofexcess fluid, and/or irrigation of the tissue. Fluid, power, and/or datalines from different sources are often entangled during the surgicalprocedure. Valuable time can be lost addressing this issue during asurgical procedure. Detangling the lines may necessitate disconnectingthe lines from their respective modules, which may require resetting themodules. The hub modular enclosure 20060 offers a unified environmentfor managing the power, data, and fluid lines, which reduces thefrequency of entanglement between such lines. Aspects of the presentdisclosure present a surgical hub 20006 for use in a surgical procedurethat involves energy application to tissue at a surgical site. Thesurgical hub 20006 includes a hub enclosure 20060 and a combo generatormodule slidably receivable in a docking station of the hub enclosure20060. The docking station includes data and power contacts. The combogenerator module includes two or more of an ultrasonic energy generatorcomponent, a bipolar RF energy generator component, and a monopolar RFenergy generator component that are housed in a single unit. In oneaspect, the combo generator module also includes a smoke evacuationcomponent, at least one energy delivery cable for connecting the combogenerator module to a surgical instrument, at least one smoke evacuationcomponent configured to evacuate smoke, fluid, and/or particulatesgenerated by the application of therapeutic energy to the tissue, and afluid line extending from the remote surgical site to the smokeevacuation component. In one aspect, the fluid line may be a first fluidline, and a second fluid line may extend from the remote surgical siteto a suction and irrigation module 20055 slidably received in the hubenclosure 20060. In one aspect, the hub enclosure 20060 may include afluid interface. Certain surgical procedures may require the applicationof more than one energy type to the tissue. One energy type may be morebeneficial for cutting the tissue, while another different energy typemay be more beneficial for sealing the tissue. For example, a bipolargenerator can be used to seal the tissue while an ultrasonic generatorcan be used to cut the sealed tissue. Aspects of the present disclosurepresent a solution where a hub modular enclosure 20060 is configured toaccommodate different generators and facilitate an interactivecommunication therebetween. One of the advantages of the hub modularenclosure 20060 is enabling the quick removal and/or replacement ofvarious modules. Aspects of the present disclosure present a modularsurgical enclosure for use in a surgical procedure that involves energyapplication to tissue. The modular surgical enclosure includes a firstenergy-generator module, configured to generate a first energy forapplication to the tissue, and a first docking station comprising afirst docking port that includes first data and power contacts, whereinthe first energy-generator module is slidably movable into an electricalengagement with the power and data contacts and wherein the firstenergy-generator module is slidably movable out of the electricalengagement with the first power and data contacts. Further to the above,the modular surgical enclosure also includes a second energy-generatormodule configured to generate a second energy, different than the firstenergy, for application to the tissue, and a second docking stationcomprising a second docking port that includes second data and powercontacts, wherein the second energy-generator module is slidably movableinto an electrical engagement with the power and data contacts, andwherein the second energy-generator module is slidably movable out ofthe electrical engagement with the second power and data contacts. Inaddition, the modular surgical enclosure also includes a communicationbus between the first docking port and the second docking port,configured to facilitate communication between the firstenergy-generator module and the second energy-generator module.Referring to FIG. 3, aspects of the present disclosure are presented fora hub modular enclosure 20060 that allows the modular integration of agenerator module 20050, a smoke evacuation module 20054, and asuction/irrigation module 20055. The hub modular enclosure 20060 furtherfacilitates interactive communication between the modules 20059, 20054,and 20055. The generator module 20050 can be a generator module 20050with integrated monopolar, bipolar, and ultrasonic components supportedin a single housing unit slidably insertable into the hub modularenclosure 20060. The generator module 20050 can be configured to connectto a monopolar device 20051, a bipolar device 20052, and an ultrasonicdevice 20053. Alternatively, the generator module 20050 may comprise aseries of monopolar, bipolar, and/or ultrasonic generator modules thatinteract through the hub modular enclosure 20060. The hub modularenclosure 20060 can be configured to facilitate the insertion ofmultiple generators and interactive communication between the generatorsdocked into the hub modular enclosure 20060 so that the generators wouldact as a single generator.

FIG. 4 illustrates a surgical data network having a set of communicationhubs configured to connect a set of sensing systems, an environmentsensing system, and a set of other modular devices located in one ormore operating theaters of a healthcare facility, a patient recoveryroom, or a room in a healthcare facility specially equipped for surgicaloperations, to the cloud, in accordance with at least one aspect of thepresent disclosure.

As illustrated in FIG. 4, a surgical hub system 20060 may include amodular communication hub 20065 that is configured to connect modulardevices located in a healthcare facility to a cloud-based system (e.g.,a cloud computing system 20064 that may include a remote server 20067coupled to a remote storage 20068). The modular communication hub 20065and the devices may be connected in a room in a healthcare facilityspecially equipped for surgical operations. In one aspect, the modularcommunication hub 20065 may include a network hub 20061 and/or a networkswitch 20062 in communication with a network router 20066. The modularcommunication hub 20065 may be coupled to a local computer system 20063to provide local computer processing and data manipulation. Surgicaldata network associated with the surgical hub system 20060 may beconfigured as passive, intelligent, or switching. A passive surgicaldata, network serves as a conduit for the data, enabling it to go fromone device (or segment) to another and to the cloud computing resources.An intelligent surgical data network includes additional features toenable the traffic passing through the surgical data network to bemonitored and to configure each port in the network hub 20061 or networkswitch 20062. An intelligent surgical data network may be referred to asa manageable hub or switch. A switching hub reads the destinationaddress of each packet and then forwards the packet to the correct port.

Modular devices 1 a-1 n located in the operating theater may be coupledto the modular communication hub 20065. The network hub 20061 and/or thenetwork switch 20062 may be coupled to a network router 20066 to connectthe devices 1 a-1 n to the cloud computing system 20064 or the localcomputer system 20063. Data associated with the devices 1 a-1 n may betransferred to cloud-based computers via the router for remote dataprocessing and manipulation. Data associated with the devices 1 a-1 nmay also be transferred to the local computer system 20063 for localdata processing and manipulation. Modular devices 2 a-2 m located in thesame operating theater also may be coupled to a network switch 20062.The network switch 20062 may be coupled to the network hub 20061 and/orthe network router 20066 to connect the devices 2a-2m to the cloud20064. Data associated with the devices 2 a-2 m may be transferred tothe cloud computing system 20064 via the network router 20066 for dataprocessing and manipulation. Data associated with the devices 2 a-2 mmay also be transferred to the local computer system 20063 for localdata processing and manipulation.

The wearable sensing system 20011 may include one or more sensingsystems 20069. The sensing systems 20069 may include a surgeon sensingsystem and/or a patient sensing system. The one or more sensing systems20069 may be in communication with the computer system 20063 of asurgical hub system 20060 or the cloud server 20067 directly via one ofthe network routers 20066 or via a network hub 20061 or networkswitching 20062 that is in communication with the network routers 20066.

The sensing systems 20069 may be coupled to the network router 20066 toconnect to the sensing systems 20069 to the local computer system 20063and/or the cloud computing system 20064. Data associated with thesensing systems 20069 may be transferred to the cloud computing system20064 via the network router 20066 for data processing and manipulation.Data associated with the sensing systems 20069 may also be transferredto the local computer system 20063 for local data processing andmanipulation.

As illustrated in FIG. 4, the surgical hub system 20060 may be expandedby interconnecting multiple network hubs 20061 and/or multiple networkswitches 20062 with multiple network routers 20066. The modularcommunication hub 20065 may be contained in a modular control towerconfigured to receive multiple devices 1 a-1 n/ 2 a-2 m. The localcomputer system 20063 also may be contained in a modular control tower.The modular communication hub 20065 may be connected to a display 20068to display images obtained by some of the devices 1 a-1 n/ 2 a-2 m, forexample during surgical procedures. In various aspects, the devices 1a-1 n/ 2 a-2 m may include, for example, various modules such as animaging module coupled to an endoscope, a generator module coupled to anenergy-based surgical device, a smoke evacuation module, asuction/irrigation module, a communication module, a processor module, astorage array, a surgical device coupled to a display, and/or anon-contact sensor module, among other modular devices that may beconnected to the modular communication hub 20065 of the surgical datanetwork.

In one aspect, the surgical hub system 20060 illustrated in FIG. 4 maycomprise a combination of network hub(s), network switch(es), andnetwork router(s) connecting the devices 1 a-1 n/ 2 a-2 m or the sensingsystems 20069 to the cloud-base system 20064. One or more of the devices1 a-1 n/ 2 a-2 m or the sensing systems 20069 coupled to the network hub20061 or network switch 20062 may collect data or measurement data inreal-time and transfer the data to cloud computers for data processingand manipulation. It will be appreciated that cloud computing relies onsharing computing resources rather than having local servers or personaldevices to handle software applications. The word “cloud” may be used asa metaphor for “the Internet,” although the term is not limited as such.Accordingly, the term “cloud computing” may be used herein to refer to“a type of Internet-based computing,” where different services—such asservers, storage, and applications—are delivered to the modularcommunication hub 20065 and/or computer system 20063 located in thesurgical theater (e.g., a fixed, mobile, temporary, or field operatingroom or space) and to devices connected to the modular communication hub20065 and/or computer system 20063 through the Internet. The cloudinfrastructure may be maintained by a cloud service provider. In thiscontext, the cloud service provider may be the entity that coordinatesthe usage and control of the devices 1 a-1 n/ 2 a-2 m located in one ormore operating theaters. The cloud computing services can perform alarge number of calculations based on the data gathered by smartsurgical instruments, robots, sensing systems, and other computerizeddevices located in the operating theater. The hub hardware enablesmultiple devices, sensing systems, and/or connections to be connected toa computer that communicates with the cloud computing resources andstorage.

Applying cloud computer data processing techniques on the data collectedthe devices 1 a-1 n/ 2 a-2 m, the surgical data network can provideimproved surgical outcomes, reduced costs, and improved patientsatisfaction. At least some of the devices 1 a-1 n/ 2 a-2 m may beemployed to view tissue states to assess leaks or perfusion of sealedtissue after a tissue sealing and cutting procedure. At least some ofthe devices 1 a-1 n/ 2 a-2 m may be employed to identify pathology, suchas the effects of diseases, using the cloud-based computing to examinedata including images of samples of body tissue for diagnostic purposes.This may include localization and margin confirmation of tissue andphenotypes. At least some of the devices 1 a-1 n/ 2 a-2 m may beemployed to identify anatomical structures of the body using a varietyof sensors integrated with imaging devices and techniques such asoverlaying images captured by multiple imaging devices. The datagathered by the devices 1 a-1 n/ 2 a-2 m, including image data, may betransferred to the cloud computing system 20064 or the local computersystem 20063 or both for data processing and manipulation includingimage processing and manipulation. The data may be analyzed to improvesurgical procedure outcomes by determining if further treatment, such asthe application of endoscopic intervention, emerging technologies, atargeted radiation, targeted intervention, and precise robotics totissue-specific sites and conditions, may be pursued. Such data analysismay further employ outcome analytics processing and using standardizedapproaches may provide beneficial feedback to either confirm surgicaltreatments and the behavior of the surgeon or suggest modifications tosurgical treatments and the behavior of the surgeon.

Applying cloud computer data processing techniques on the measurementdata collected by the sensing systems 20069, the surgical data networkcan provide improved surgical outcomes, improved recovery outcomes,reduced costs, and improved patient satisfaction. At least some of thesensing systems 20069 may be employed to assess physiological conditionsof a surgeon operating on a patient or a patient being prepared for asurgical procedure or a patient recovering after a surgical procedure.The cloud-based computing system 20064 may be used to monitor biomarkersassociated with a surgeon or a patient in real-time and to generatesurgical plans based at least on measurement data gathered prior to asurgical procedure, provide control signals to the surgical instrumentsduring a surgical procedure, notify a patient of a complication duringpost-surgical period.

The operating theater devices 1 a-1 n/ 2 a-2 m may be connected to themodular communication hub 20065 over a wired channel or a wirelesschannel depending on the configuration of the devices 1 a-1 n to anetwork hub 20061. The network hub 20061 may be implemented, in oneaspect, as a local network broadcast device that works on the physicallayer of the Open System Interconnection (OSI) model. The network hubmay provide connectivity to the devices 1 a-1 n located in the sameoperating theater network. The network hub 20061 may collect data in theform of packets and sends them to the router in half duplex mode. Thenetwork hub 20061 may not store any media access control/InternetProtocol (MAC/IP) to transfer the device data. Only one of the devices 1a-1 n can send data at a time through the network hub 20061. The networkhub 20061 may not have routing tables or intelligence regarding where tosend information and broadcasts all network data across each connectionand to a remote server 20067 of the cloud computing system 20064. Thenetwork hub 20061 can detect basic network errors such as collisions buthaving all information broadcast to multiple ports can be a securityrisk and cause bottlenecks.

The operating theater devices 2 a-2 m may be connected to a networkswitch 20062 over a wired channel or a wireless channel. The networkswitch 20062 works in the data link layer of the OSI model. The networkswitch 20062 may be a multicast device for connecting the devices 2 a-2m located in the same operating theater to the network. The networkswitch 20062 may send data in the form of frames to the network router20066 and may work in full duplex mode. Multiple devices 2 a-2 m cansend data at the same time through the network switch 20062. The networkswitch 20062 stores and uses MAC addresses of the devices 2 a-2 m totransfer data.

The network hub 20061 and/or the network switch 20062 may be coupled tothe network router 20066 for connection to the cloud computing system20064. The network router 20066 works in the network layer of the OSImodel. The network router 20066 creates a route for transmitting datapackets received from the network hub 20061 and/or network switch 20062to cloud-based computer resources for further processing andmanipulation of the data collected by any one of or the devices 1 a-1 n/2 a-2 m and wearable sensing system 20011. The network router 20066 maybe employed to connect two or more different networks located indifferent locations, such as, for example, different operating theatersof the same healthcare facility or different networks located indifferent operating theaters of different healthcare facilities. Thenetwork router 20066 may send data in the form of packets to the cloudcomputing system 20064 and works in full duplex mode. Multiple devicescan send data at the same time. The network router 20066 may use IPaddresses to transfer data.

In an example, the network hub 20061 may be implemented as a USB hub,which allows multiple USB devices to be connected to a host computer.The USB hub may expand a single USB port into several tiers so thatthere are more ports available to connect devices to the host systemcomputer. The network hub 20061 may include wired or wirelesscapabilities to receive information over a wired channel or a wirelesschannel. In one aspect, a wireless USB short-range, high-bandwidthwireless radio communication protocol may be employed for communicationbetween the devices 1 a-1 n and devices 2 a-2 m located in the operatingtheater.

In examples, the operating theater devices 1 a-1 n/ 2 a-2 m and/or thesensing systems 20069 may communicate to the modular communication hub20065 via Bluetooth wireless technology standard for exchanging dataover short distances (using short-wavelength UHF radio waves in the ISMband from 2.4 to 2.485 GHz) from fixed and mobile devices and buildingpersonal area networks (PANs). The operating theater devices 1 a-1 n/ 2a-2 m and/or the sensing systems 20069 may communicate to the modularcommunication hub 20065 via a number of wireless or wired communicationstandards or protocols, including but not limited to Bluetooth,Low-Energy Bluetooth, near-field communication (NFC), Wi-Fi (IEEE 802.11family), WiMAX (IEEE 802.16 family), IEEE 802.20, new radio (NR),long-term evolution (LTE), and Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM,GPRS, CDMA, TDMA, DECT, and Ethernet derivatives thereof, as well as anyother wireless and wired protocols that are designated as 3G, 4G, 5G,and beyond. The computing module may include a plurality ofcommunication modules. For instance, a first communication module may bededicated to shorter-range wireless communications such as Wi-Fi andBluetooth Low-Energy Bluetooth, Bluetooth Smart, and a secondcommunication module may be dedicated to longer-range wirelesscommunications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, HSPA+,HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, and others.

The modular communication hub 20065 may serve as a central connectionfor one or more of the operating theater devices 1 a-1 n/ 2 a-2 m and/orthe sensing systems 20069 and may handle a data type known as frames.Frames may carry the data generated by the devices 1 a-1 n/ 2 a-2 mand/or the sensing systems 20069. When a frame is received by themodular communication hub 20065, it may be amplified and/or sent to thenetwork router 20066, which may transfer the data to the cloud computingsystem 20064 or the local computer system 20063 by using a number ofwireless or wired communication standards or protocols, as describedherein.

The modular communication hub 20065 can be used as a standalone deviceor be connected to compatible network hubs 20061 and network switches20062 to form a larger network. The modular communication hub 20065 canbe generally easy to install, configure, and maintain, making it a goodoption for networking the operating theater devices 1 a-1 n/ 2 a-2 m.

FIG. 5 illustrates a computer-implemented interactive surgical system20070 that may be a part of the surgeon monitoring system 20002. Thecomputer-implemented interactive surgical system 20070 is similar inmany respects to the surgeon sensing system 20002. For example, thecomputer-implemented interactive surgical system 20070 may include oneor more surgical sub-systems 20072, which are similar in many respectsto the surgeon monitoring systems 20002. Each sub-surgical system 20072includes at least one surgical hub 20076 in communication with a cloudcomputing system 20064 that may include a remote server 20077 and aremote storage 20078. In one aspect, the computer-implementedinteractive surgical system 20070 may include a modular control tower20085 connected to multiple operating theater devices such as sensingsystems (e.g., surgeon sensing systems 20002 and/or patient sensingsystem 20003), intelligent surgical instruments, robots, and othercomputerized devices located in the operating theater. As shown in FIG.6A, the modular control tower 20085 may include a modular communicationhub 20065 coupled to a local computing system 20063.

As illustrated in the example of FIG. 5, the modular control tower 20085may be coupled to an imaging module 20088 that may be coupled to anendoscope 20087, a generator module 20090 that may be coupled to anenergy device 20089, a smoke evacuator module 20091, asuction/irrigation module 20092, a communication module 20097, aprocessor module 20093, a storage array 20094, a smart device/instrument20095 optionally coupled to a display 20086 and 20084 respectively, anda non-contact sensor module 20096. The modular control tower 20085 mayalso be in communication with one or more sensing systems 20069 and anenvironmental sensing system 20015. The sensing systems 20069 may beconnected to the modular control tower 20085 either directly via arouter or via the communication module 20097. The operating theaterdevices may be coupled to cloud computing resources and data storage viathe modular control tower 20085. A robot surgical hub 20082 also may beconnected to the modular control tower 20085 and to the cloud computingresources. The devices/instruments 20095 or 20084, human interfacesystem 20080, among others, may be coupled to the modular control tower20085 via wired or wireless communication standards or protocols, asdescribed herein. The human interface system 20080 may include a displaysub-system and a notification sub-system. The modular control tower20085 may be coupled to a hub display 20081 (e.g., monitor, screen) todisplay and overlay images received from the imaging module 20088,device/instrument display 20086, and/or other human interface systems20080. The hub display 20081 also may display data received from devicesconnected to the modular control tower 20085 in conjunction with imagesand overlaid images.

FIG. 6A illustrates a surgical hub 20076 comprising a plurality ofmodules coupled to the modular control tower 20085. As shown in FIG. 6A,the surgical hub 20076 may be connected to a generator module 20090, thesmoke evacuator module 20091, suction/irrigation module 20092, and thecommunication module 20097. The modular control tower 20085 may comprisea modular communication hub 20065, e.g., a network connectivity device,and a computer system 20063 to provide local wireless connectivity withthe sensing systems, local processing, complication monitoring,visualization, and imaging, for example. As shown in FIG. 6A, themodular communication hub 20065 may be connected in a configuration(e.g., a tiered configuration) to expand a number of modules (e.g.,devices) and a number of sensing systems 20069 that may be connected tothe modular communication hub 20065 and transfer data associated withthe modules and/or measurement data associated with the sensing systems20069 to the computer system 20063, cloud computing resources, or both.As shown in FIG. 6A, each of the network hubs/switches 20061/20062 inthe modular communication hub 20065 may include three downstream portsand one upstream port. The upstream network hub/switch may be connectedto a processor 20102 to provide a communication connection to the cloudcomputing resources and a local display 20108. At least one of thenetwork/hub switches 20061/20062 in the modular communication hub 20065may have at least one wireless interface to provided communicationconnection between the sensing systems 20069 and/or the devices 20095and the cloud computing system 20064. Communication to the cloudcomputing system 20064 may be made either through a wired or a wirelesscommunication channel.

The surgical hub 20076 may employ a non-contact sensor module 20096 tomeasure the dimensions of the operating theater and generate a map ofthe surgical theater using either ultrasonic or laser-type non-contactmeasurement devices. An ultrasound-based non-contact sensor module mayscan the operating theater by transmitting a burst of ultrasound andreceiving the echo when it bounces off the perimeter walls of anoperating theater as described under the heading “Surgical Hub SpatialAwareness Within an Operating Room” in U.S. Provisional PatentApplication Ser. No. 62/611,341, titled INTERACTIVE SURGICAL, PLATFORM,filed Dec. 28, 2017, which is herein incorporated by reference in itsentirety, in which the sensor module is configured to determine the sizeof the operating theater and to adjust Bluetooth-pairing distancelimits. A laser-based non-contact sensor module may scan the operatingtheater by transmitting laser light pulses, receiving laser light pulsesthat bounce off the perimeter walls of the operating theater, andcomparing the phase of the transmitted pulse to the received pulse todetermine the size of the operating theater and to adjust Bluetoothpairing distance limits, for example.

The computer system 20063 may comprise a processor 20102 and a networkinterface 20100. The processor 20102 may be coupled to a communicationmodule 20103, storage 20104, memory 20105, non-volatile memory 20106,and input/output (I/O) interface 20107 via a system bus. The system buscan be any of several types of bus structure(s) including the memory busor memory controller, a peripheral bus or external bus, and or a localbus using any variety of available bus architectures including, but notlimited to, 9-bit bus, Industrial Standard Architecture (ISA),Micro-Charmel Architecture (MSA), Extended ISA (EISA), Intelligent DriveElectronics (IDE), VESA Local Bus (VLB), Peripheral ComponentInterconnect (PCI), USB, Advanced Graphics Port (AGP), Personal ComputerMemory Card International Association bus (PCMCIA), Small ComputerSystems interface (SCSI), or any other proprietary bus.

The processor 20102 may be any single-core or multicore processor suchas those known under the trade name ARM Cortex by Texas Instruments. Inone aspect, the processor may be an LM4F230H5QR ARM Cortex-M4F ProcessorCore, available from Texas Instruments, for example, comprising anon-chip memory of 256 KB single-cycle flash memory, or othernon-volatile memory, up to 40 MHz, a prefetch buffer to improveperformance above 40 MHz, a 32 KB single-cycle serial random accessmemory (SRAM), an internal read-only memory (ROM) loaded withStellarisWare® software, a 2 KB electrically erasable programmableread-only memory (EEPROM), and/or one or more pulse width modulation(PWM) modules, one or more quadrature encoder inputs (QEI) analogs, oneor more 12-bit analog-to-digital converters (ADCs) with 12 analog inputchannels, details of which are available for the product datasheet.

In an example, the processor 20102 may comprise a safety controllercomprising two controller-based families such as TMS570 and RM4x, knownunder the trade name Hercules ARM Cortex R4, also by Texas Instruments.The safety controller may be configured specifically for IEC 61508 andISO 26262 safety critical applications, among others, to provideadvanced integrated safety features while delivering scalableperformance, connectivity, and memory options.

The system memory may include volatile memory and non-volatile memory.The basic input/output system (BIOS), containing the basic routines totransfer information between elements within the computer system, suchas during start-up, is stored in non-volatile memory. For example, thenon-volatile memory can include ROM, programmable ROM (PROM),electrically programmable ROM (EPROM), EEPROM, or flash memory. Volatilememory includes random-access memory (RAM), which acts as external cachememory. Moreover, RAM is available in many forms such as SRAM, dynamicRAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and directRambus RAM (DRRAM).

The computer system 20063 also may include removable/non-removable,volatile/non-volatile computer storage media, such as for example diskstorage. The disk storage can include, but is not limited to, deviceslike a magnetic disk drive, floppy disk drive, tape drive, Jaz drive,Zip drive, LS-60 drive, flash memory card, or memory stick. In addition,the disk storage can include storage media separately or in combinationwith other storage media including, but not limited to, an optical discdrive such as a compact disc ROM device (CD-ROM), compact discrecordable drive (CD-R Drive), compact disc rewritable drive (CD-RWDrive), or a digital versatile disc ROM drive (DVD-ROM). To facilitatethe connection of the disk storage devices to the system bus, aremovable or non-removable interface may be employed.

It is to be appreciated that the computer system 20063 may includesoftware that acts as an intermediary between users and the basiccomputer resources described in a suitable operating environment. Suchsoftware may include an operating system. The operating system, whichcan be stored on the disk storage, may act to control and allocateresources of the computer system. System applications may take advantageof the management of resources by the operating system through programmodules and program data stored either in the system memory or on thedisk storage. It is to be appreciated that various components describedherein can be implemented with various operating systems or combinationsof operating systems

A user may enter commands of information into the computer system 20063through input device(s) coupled to the I/O interface 20107. The inputdevices may include, but are not limited to, a pointing device such as amouse, trackball, stylus, touch pad, keyboard, microphone, joystick,game pad, satellite dish, scanner, TV tuner card, digital camera,digital video camera, web camera, and the like. These and other inputdevices connect to the processor 20102 through the system bus viainterface port(s). The interface port(s) include, for example, a serialport, a parallel port, a game port, and a USB. The output device(s) usesome of the same types of ports as input device(s). Thus, for example, aUSB port may be used to provide input to the computer system 20063 andto output information from the computer system 20063 to an outputdevice. An output adapter may be provided to illustrate that there canbe some output devices like monitors, displays, speakers, and printers,among other output devices that may require special adapters. The outputadapters may include, by way of illustration and not limitation, videoand sound cards that provide a means of connection between the outputdevice and the system bus. It should be noted that other devices and/orsystems of devices, such as remote computer(s), may provide both inputand output capabilities.

The computer system 20063 can operate in a networked environment usinglogical connections to one or more remote computers, such as cloudcomputer(s), or local computers. The remote cloud computer(s) can be apersonal computer, server, router, network PC, workstation,microprocessor-based appliance, peer deice, or other common networknode, and the like, and typically includes many or all of the elementsdescribed relative to the computer system. For purposes of brevity, onlya memory storage device is illustrated with the remote computer(s). Theremote computer(s) may be logically connected to the computer systemthrough a network interface and then physically connected via acommunication connection. The network interface may encompasscommunication networks such as local area networks (LANs) and wide areanetworks (WANs). LAN technologies may include Fiber Distributed DataInterface (FDDI), Copper Distributed Data interface (CDDI),Ethernet/IEEE 802.3, Token Ring/IEEE 802.5, and the like. WANtechnologies may include, but are not limited to, point-to-point links,circuit-switching networks like Integrated Services Digital Networks(ISDN) and variations thereon, packet-switching networks, and DigitalSubscriber Lines (DSL).

In various examples, the computer system 20063 of FIG. 4, FIG. 6A andFIG. 6B, the imaging module 20088 and/or human interface system 20080,and/or the processor module 20093 of FIG. 5 and FIG. 6A may comprise animage processor, image-processing engine, media processor, or anyspecialized digital signal processor (DSP) used for the processing ofdigital images. The image processor may employ parallel computing withsingle instruction, multiple data (SIMD) or multiple instruction,multiple data (MIMD) technologies to increase speed and efficiency. Thedigital image-processing engine can perform a range of tasks. The imageprocessor may be a system on a chip with multicore processorarchitecture.

The communication connection(s) may refer to the hardware/softwareemployed to connect the network interface to the bus. While thecommunication connection is shown for illustrative clarity inside thecomputer system 20063, it can also be external to the computer system20063. The hardware/software necessary for connection to the networkinterface may include, for illustrative purposes only, internal andexternal technologies such as modems, including regular telephone grademodems, cable modems, optical fiber modems, and DSL modems, ISDNadapters, and Ethernet cards. In some examples, the network interfacemay also be provided using an RF interface.

FIG. 6B illustrates an example of a wearable monitoring system, e.g., acontrolled patient monitoring system. A controlled patient monitoringsystem may be the sensing system used to monitor a set of patientbiomarkers when the patient is at a healthcare facility. The controlledpatient monitoring system may be deployed for pre-surgical patientmonitoring when a patient is being prepared for a surgical procedure,in-surgical monitoring when a patient is being operated on, or inpost-surgical monitoring, for example, when a patient is recovering,etc. As illustrated in FIG. 6B, a controlled patient monitoring systemmay include a surgical hub system 20076, which may include one or morerouters 20066 of the modular communication hub 20065 and a computersystem 20063. The routers 20065 may include wireless routers, wiredswitches, wired routers, wired or wireless networking hubs, etc. In anexample, the routers 20065 may be part of the infrastructure. Thecomputing system 20063 may provide local processing for monitoringvarious biomarkers associated with a patient or a surgeon, and anotification mechanism to indicate to the patient and/or a healthcareprovided (HCP) that a milestone (e.g., a recovery milestone) is met or acomplication is detected. The computing system 20063 of the surgical hubsystem 20076 may also be used to generate a severity level associatedwith the notification, for example, a notification that a complicationhas been detected.

The computing system 20063 of FIG. 4, FIG. 6B, the computing device20200 of FIG. 6C, the hub/computing device 20243 of FIG. 7B, FIG. 7C, orFIG. 7D may be a surgical computing system or a hub device, a laptop, atablet, a smart phone, etc.

As shown to FIG. 6B, a set of sensing systems 20069 and/or anenvironmental sensing system 20015 (as described in FIG. 2A) may beconnected to the surgical hub system 20076 via the routers 20065. Therouters 20065 may also provide a direct communication connection betweenthe sensing systems 20069 and the cloud computing system 20064, forexample, without involving the local computer system 20063 of thesurgical hub system 20076. Communication from the surgical hub system20076 to the cloud 20064 may be made either through a wired or awireless communication channel.

As shown in FIG. 6B, the computer system 20063 may include a processor20102 and a network interface 20100. The processor 20102 may be coupledto a radio frequency (RF) interface or a communication module 20103,storage 20104, memory 20105, non-volatile memory 20106, and input/outputinterface 20107 via a system bus, as described in FIG. 6A. The computersystem 20063 may be connected with a local display unit 20108. In someexamples, the display unit 20108 may be replaced by a HID. Details aboutthe hardware and software components of the computer system are providedin FIG. 6A.

As shown in FIG. 6B, a sensing system 20069 may include a processor20110. The processor 20110 may be coupled to a radio frequency (RF)interface 20114, storage 20113, memory (e.g., a non-volatile memory)20112, and I/O interface 20111 via a system bus. The system bus can beany of several types of bus structure(s) including the memory bus ormemory controller, a peripheral bus or external bus, and/or a local bus,as described herein. The processor 20110 may be any single-core ormulticore processor as described herein.

It is to be appreciated that the sensing system 20069 may includesoftware that acts as an intermediary between sensing system users andthe computer resources described in a suitable operating environment.Such software may include an operating system. The operating system,which can be stored on the disk storage, may act to control and allocateresources of the computer system. System applications may take advantageof the management of resources by the operating system through programmodules and program data stored either in the system memory or on thedisk storage. It is to be appreciated that various components describedherein can be implemented with various operating systems or combinationsof operating systems.

The sensing system 20069 may be connected to a human interface system20115. The human interface system 20115 may be a touch screen display.The human interface system 20115 may include a human interface displayfor displaying information associated with a surgeon biomarker and/or apatient biomarker, display a prompt for a user action by a patient or asurgeon, or display a notification to a patient or a surgeon indicatinginformation about a recovery millstone or a complication. The humaninterface system 20115 may be used to receive input from a patient or asurgeon. Other human interface systems may be connected to the sensingsystem 20069 via the I/O interface 20111. For example, the humaninterface device 20115 may include devices for providing a hapticfeedback as a mechanism for prompting a user to pay attention to anotification that may be displayed on a display unit.

The sensing system 20069 may operate in a networked environment usinglogical connections to one or more remote computers, such as cloudcomputer(s), or local computers. The remote cloud computer(s) can be apersonal computer, server, router, network PC, workstation,microprocessor-based appliance, peer device, or other common networknode, and the like, and typically includes many or all of the elementsdescribed relative to the computer system. The remote computer(s) may belogically connected to the computer system through a network interface.The network interface may encompass communication networks such as localarea networks (LANs), wide area networks (WANs), and/or mobile networks.LAN technologies may include Fiber Distributed Data Interface (FDDI),Copper Distributed Data interface (CDDI), Ethernet/IEEE 802.3, TokenRing/IEEE 802.5, Wi-Fi/IEEE 802.11, and the like. WAN technologies mayinclude, but are not limited to, point-to-point links, circuit-switchingnetworks like Integrated Services Digital Networks (ISDN) and variationsthereon, packet-switching networks, and Digital Subscriber Lines (DSL).The mobile networks may include communication links based on one or moreof the following mobile communication protocols: GSM/GPRS/EDGE (2G),UMTS/HSPA (3G), long term evolution (LTE) or 4G, LTE-Advanced (LTE-A),new radio (NR) or 5G, etc.

FIG. 6C illustrates an exemplary uncontrolled patient monitoring system,for example, when the patient is away from a healthcare facility. Theuncontrolled patient monitoring system may be used for pre-surgicalpatient monitoring when a patient is being prepared for a surgicalprocedure but is away from a healthcare facility, or in post-surgicalmonitoring, for example, when a patient is recovering away from ahealthcare facility.

As illustrated in FIG. 6C, one or more sensing systems 20069 are incommunication with a computing device 20200, for example, a personalcomputer, a laptop, a tablet, or a smart phone. The computing system20200 may provide processing for monitoring of various biomarkersassociated with a patient, a notification mechanism to indicate that amilestone (e.g., a recovery milestone) is met or a complication isdetected. The computing system 20200 may also provide instructions forthe user of the sensing system to follow. The communication between thesensing systems 20069 and the computing device 20200 may be establisheddirectly using a wireless protocol as described herein or via thewireless router/hub 20211.

As shown in FIG. 6C, the sensing systems 20069 may be connected to thecomputing device 20200 via router 20211. The router 20211 may includewireless routers, wired switches, wired routers, wired or wirelessnetworking hubs, etc. The router 20211 may provide a directcommunication connection between the sensing systems 20069 and the cloudservers 20064, for example, without involving the local computing device20200. The computing device 20200 may be in communication with the cloudserver 20064. For example, the computing device 20200 may be incommunication with the cloud 20064 through a wired or a wirelesscommunication channel. In an example, a sensing system 20069 may be incommunication with the cloud directly over a cellular network, forexample, via a cellular base station 20210.

As shown in FIG. 6C, the computing device 20200 may include a processor20203 and a network or an RF interface 20201. The processor 20203 may becoupled to a storage 20202, memory 20212, nonvolatile memory 20213, andinput/output interface 20204 via a system bus, as described in FIG. 6Aand FIG. 6B. Details about the hardware and software components of thecomputer system are provided in FIG. 6A. The computing device 20200 mayinclude a set of sensors, for example, sensor #1 20205, sensor #2 20206up to sensor #n 20207. These sensors may be a part of the computingdevice 20200 and may be used to measure one or more attributesassociated with the patient. The attributes may provide a context abouta biomarker measurement performed by one of the sensing systems 20069.For example, sensor #1 may be an accelerometer that may be used tomeasure acceleration forces in order to sense movement or vibrationsassociated with the patient. In an example, the sensors 20205 to 20207may include one or more of: a pressure sensor, an altimeter, athermometer, a lidar, or the like.

As shown in FIG. 6B, a sensing system 20069 may include a processor, aradio frequency interface, a storage, a memory or non-volatile memory,and input/output interface via a system bus, as described in FIG. 6A.The sensing system may include a sensor unit and a processing andcommunication unit, as described in FIG. 7B through 7D. The system buscan be any of several types of bus structure(s) including the memory busor memory controller, a peripheral bus or external bus, and/or a localbus, as described herein. The processor may be any single-core ormulticore processor, as described herein.

The sensing system 20069 may be in communication with a human interfacesystem 20215. The human interface system 20215 may be a touch screendisplay. The human interface system 210215 may be used to displayinformation associated with a patient biomarker, display a prompt for auser action by a patient, or display a notification to a patientindicating information about a recovery millstone or a complication. Thehuman interface system 20215 may be used to receive input from apatient. Other human interface systems may be connected to the sensingsystem 20069 via the I/O interface. For example, the human interfacesystem may include devices for providing a haptic feedback as amechanism for prompting a user to pay attention to a notification thatmay be displayed on a display unit. The sensing system 20069 may operatein a networked environment using logical connections to one or moreremote computers, such as cloud computer(s), or local computers, asdescribed in FIG. 6B,

FIG. 7A illustrates a logical diagram of a control system 20220 of asurgical instrument or a surgical tool accordance with one or moreaspects of the present disclosure. The surgical instrument or thesurgical tool may be configurable. The surgical instrument may includesurgical fixtures specific to the procedure at-hand, such as imagingdevices, surgical staplers, energy devices, endocutter devices, or thelike. For example, the surgical instrument may include any of a poweredstapler, a powered stapler generator, an energy device, an advancedenergy device, an advanced energy jaw device, an endocutter clamp, anenergy device generator, an in-operating-room imaging system, a smokeevacuator, a suction-irrigation device, an insufflation system, or thelike. The system 20220 may comprise a control circuit. The controlcircuit may include a microcontroller 20221 comprising a processor 20222and a memory 20223. One or more of sensors 20225, 20226, 20227, forexample, provide real-time feedback to the processor 20222. A motor20230, driven by a motor driver 20229, operably couples a longitudinallymovable displacement member to drive the I-beam knife element. Atracking system 20228 may be configured to determine the position of thelongitudinally movable displacement member. The position information maybe provided to the processor 20222, which can be programmed orconfigured to determine the position of the longitudinally movable drivemember as well as the position of a firing member, firing bar, andI-beam knife element. Additional motors may be provided at the tooldriver interface to control I-beam firing, closure tube travel, shaftrotation, and articulation. A display 20224 may display a variety ofoperating conditions of the instruments and may include touch screenfunctionality for data input. Information displayed on the display 20224may be overlaid with images acquired via endoscopic imaging modules.

In one aspect, the microcontroller 20221 may be any single core ormulticore processor such as those known under the trade name ARM Cortexby Texas Instruments. In one aspect, the main microcontroller 20221 maybe an LM4F230H5QR ARM Cortex-M4F Processor Core, available from TexasInstruments, for example, comprising an on-chip memory of 256 KBsingle-cycle flash memory, or other non-volatile memory, up to 40 MHz, aprefetch buffer to improve performance above 40 MHz, a 32 KBsingle-cycle SRAM, and internal ROM loaded with StellarisWare® software,a 2 KB EEPROM, one or more PWM modules, one or more QEI analogs, and/orone or more 12-bit ADCs with 12 analog input channels, details of whichare available for the product datasheet.

In one aspect, the microcontroller 20221 may comprise a safetycontroller comprising two controller-based families such as TMS570 andRM4x, known under the trade name Hercules ARM Cortex R4, also by TexasInstruments. The safety controller may be configured specifically forIEC 61508 and ISO 26262 safety critical applications, among others, toprovide advanced integrated safety features while delivering scalableperformance, connectivity, and memory options.

The microcontroller 20221 may be programmed to perform various functionssuch as precise control over the speed and position of the knife andarticulation systems. In one aspect, the microcontroller 20221 mayinclude a processor 20222 and a memory 20223. The electric motor 20230may be a brushed direct current (DC) motor with a gearbox and mechanicallinks to an articulation or knife system. in one aspect, a motor driver20229 may be an A3941 available from Allegro Microsystems, Inc. Othermotor drivers may be readily substituted for use in the tracking system20228 comprising an absolute positioning system. A detailed descriptionof an absolute positioning system is described in U.S. PatentApplication Publication No. 2017/0296213, titled SYSTEMS AND METHODS FORCONTROLLING A SURGICAL STAPLING AND CUTTING INSTRUMENT, which publishedon Oct. 19, 2017, which is herein incorporated by reference in itsentirety.

The microcontroller 20221 may be programmed to provide precise controlover the speed and position of displacement members and articulationsystems. The microcontroller 20221 may be configured to compute aresponse in the software of the microcontroller 20221. The computedresponse may be compared to a measured response of the actual system toobtain an “observed” response, which is used for actual feedbackdecisions. The observed response may be a favorable, tuned value thatbalances the smooth, continuous nature of the simulated response withthe measured response, which can detect outside influences on thesystem.

In some examples, the motor 20230 may be controlled by the motor driver20229 and can be employed by the firing system of the surgicalinstrument or tool. In various forms, the motor 20230 may be a brushedDC driving motor having a maximum rotational speed of approximately25,000 RPM. In some examples, the motor 20230 may include a brushlessmotor, a cordless motor, a synchronous motor, a stepper motor, or anyother suitable electric motor. The motor driver 20229 may comprise anH-bridge driver comprising field-effect transistors (FETs), for example.The motor 20230 can be powered by a power assembly releasably mounted tothe handle assembly or tool housing for supplying control power to thesurgical instrument or tool. The power assembly may comprise a batterywhich may include a number of battery cells connected in series that canbe used as the power source to power the surgical instrument or tool. Incertain circumstances, the battery cells of the power assembly may bereplaceable and/or rechargeable. In at least one example, the batterycells can be lithium-ion batteries which can be couplable to andseparable from the power assembly.

The motor driver 20229 may be an A3941 available from AllegroMicrosystems, Inc. A3941 may be a full-bridge controller for use withexternal N-channel power metal-oxide semiconductor field-effecttransistors (MOSFETs) specifically designed for inductive loads, such asbrush DC motors. The driver 20229 may comprise a unique charge pumpregulator that can provide full (>10 V) gate drive for battery voltagesdown to 7 V and can allow the A3941 to operate with a reduced gatedrive, down to 5.5 V. A bootstrap capacitor may be employed to providethe above battery supply voltage required for N-channel MOSFETs. Aninternal charge pump for the high-side drive may allow DC (100% dutycycle) operation. The full bridge can be driven in fast or slow decaymodes using diode or synchronous rectification. In the slow decay mode,current recirculation can be through the high-side or the low-side FETs.The power FETs may be protected from shoot-through byresistor-adjustable dead time. Integrated diagnostics provideindications of undervoltage, overtemperature, and power bridge faultsand can be configured to protect the power MOSFETs under most shortcircuit conditions. Other motor drivers may be readily substituted foruse in the tracking system 20228 comprising an absolute positioningsystem.

The tracking system 20228 may comprise a controlled motor drive circuitarrangement comprising a position sensor 20225 according to one aspectof this disclosure. The position sensor 20225 for an absolutepositioning system may provide a unique position signal corresponding tothe location of a displacement member. In some examples, thedisplacement member may represent a longitudinally movable drive membercomprising a rack of drive teeth for meshing engagement with acorresponding drive gear of a gear reducer assembly. In some examples,the displacement member may represent the firing member, which could beadapted and configured to include a rack of drive teeth. In someexamples, the displacement member may represent a firing bar or theI-beam, each of which can be adapted and configured to include a rack ofdrive teeth. Accordingly, as used herein, the term displacement membercan be used generically to refer to any movable member of the surgicalinstrument or tool such as the drive member, the firing member, thefiring bar, the I-beam, or any element that can be displaced. In oneaspect, the longitudinally movable drive member can be coupled to thefiring member, the firing bar, and the I-beam. Accordingly, the absolutepositioning system can, in effect, track the linear displacement of theI-beam by tracking the linear displacement of the longitudinally movabledrive member. In various aspects, the displacement member may be coupledto any position sensor 20225 suitable for measuring linear displacement.Thus, the longitudinally movable drive member, the firing member, thefiring bar, or the I-beam, or combinations thereof, may be coupled toany suitable linear displacement sensor. Linear displacement sensors mayinclude contact or non-contact displacement sensors. Linear displacementsensors may comprise linear variable differential transformers (LVDT),differential variable reluctance transducers (DVRT), a slidepotentiometer, a magnetic sensing system comprising a movable magnet anda series of linearly arranged Hall effect sensors, a magnetic sensingsystem comprising a fixed magnet and a series of movable, linearlyarranged Hall effect sensors, an optical sensing system comprising amovable light source and a series of linearly arranged photo diodes orphoto detectors, an optical sensing system comprising a fixed lightsource and a series of movable linearly, arranged photodiodes orphotodetectors, or any combination thereof.

The electric motor 20230 can include a rotatable shaft that operablyinterfaces with a gear assembly that is mounted in meshing engagementwith a set, or rack, of drive teeth on the displacement member. A sensorelement may be operably coupled to a gear assembly such that a singlerevolution of the position sensor 20225 element corresponds to somelinear longitudinal translation of the displacement member. Anarrangement of gearing and sensors can be connected to the linearactuator, via a rack and pinion arrangement, or a rotary actuator, via aspur gear or other connection. A power source may supply power to theabsolute positioning system and an output indicator may display theoutput of the absolute positioning system. The displacement member mayrepresent the longitudinally movable drive member comprising a rack ofdrive teeth formed thereon for meshing engagement with a correspondingdrive gear of the gear reducer assembly. The displacement member mayrepresent the longitudinally movable firing member, firing bar, I-beam,or combinations thereof.

A single revolution of the sensor element associated with the positionsensor 20225 may be equivalent to a longitudinal linear displacement d1of the of the displacement member, where d1 is the longitudinal lineardistance that the displacement member moves from point “a” to point “b”after a single revolution of the sensor element coupled to thedisplacement member. The sensor arrangement may be connected via a gearreduction that results in the position sensor 20225 completing one ormore revolutions for the full stroke of the displacement member. Theposition sensor 20225 may complete multiple revolutions for the fullstroke of the displacement member.

A series of switches, where n is an integer greater than one, may beemployed alone or in combination with a gear reduction to provide aunique position signal for more than one revolution of the positionsensor 20225. The state of the switches may be fed back to themicrocontroller 20221 that applies logic to determine a unique positionsignal corresponding to the longitudinal linear displacement d1+d2+ . .. dn of the displacement member. The output of the position sensor 20225is provided to the microcontroller 20221, The position sensor 20225 ofthe sensor arrangement may comprise a magnetic sensor, an analog rotarysensor like a potentiometer, or an array of analog Hall-effect elements,which output a unique combination of position signals or values.

The position sensor 20225 may comprise any number of magnetic sensingelements, such as, for example, magnetic sensors classified according towhether they measure the total magnetic field or the vector componentsof the magnetic field. The techniques used to produce both types ofmagnetic sensors may encompass many aspects of physics and electronics.The technologies used for magnetic field sensing may include searchcoil, fluxgate, optically pumped, nuclear precession, SQUID,Hall-effect, anisotropic magnetoresistance, giant magnetoresistance,magnetic tunnel junctions, giant magnetoimpedance,magnetostrictive/piezoelectric composites, magnetodiode,magnetotransistor, fiber-optic, magneto-optic, andmicroelectromechanical systems-based magnetic sensors, among others.

In one aspect, the position sensor 20225 for the tracking system 20228comprising an absolute positioning system may comprise a magnetic rotaryabsolute positioning system. The position sensor 20225 may beimplemented as an AS5055EQFT single-chip magnetic rotary position sensoravailable from Austria Microsystems, AG. The position sensor 20225 isinterfaced with the microcontroller 20221 to provide an absolutepositioning system. The position sensor 20225 may be a low-voltage andlow-power component and may include four Hall-effect elements in an areaof the position sensor 20225 that may be located above a magnet. Ahigh-resolution ADC and a smart power management controller may also beprovided on the chip. A coordinate rotation digital computer (CORDIC)processor, also known as the digit-by-digit method and Volder'salgorithm, may be provided to implement a simple and efficient algorithmto calculate hyperbolic and trigonometric functions that require onlyaddition, subtraction, bit-shift, and table lookup operations. The angleposition, alarm bits, and magnetic field information may be transmittedover a standard serial communication interface, such as a serialperipheral interface (SPI) interface, to the microcontroller 20221. Theposition sensor 20225 may provide 12 or 14 bits of resolution. Theposition sensor 20225 may be an AS5055 chip provided in a small QFN16-pin 4×4×0.85 mm package.

The tracking system 20228 comprising an absolute positioning system maycomprise and/or be programmed to implement a feedback controller, suchas a PID, state feedback, and adaptive controller. A power sourceconverts the signal from the feedback controller into a physical inputto the system: in this case the voltage.

Other examples include a PWM of the voltage, current, and force. Othersensor(s) may be provided to measure physical parameters of the physicalsystem in addition to the position measured by the position sensor20225. In some aspects, the other sensor(s) can include sensorarrangements such as those described in U.S. Pat. No. 9,345,481, titledSTAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM, which issued on May 24,2016, which is herein incorporated by reference in its entirety; U.S.Patent Application Publication No. 2014/0263552, titled STAPLE CARTRIDGETISSUE THICKNESS SENSOR SYSTEM, which published on Sep. 18, 2014, whichis herein incorporated by reference in its entirety; and U.S. patentapplication Ser. No. 15/628,175, titled TECHNIQUES FOR ADAPTIVE CONTROLOF MOTOR VELOCITY OF A SURGICAL STAPLING AND CUTTING INSTRUMENT, filedJun. 20, 2017, which is herein incorporated by reference in itsentirety. In a digital signal processing system, an absolute positioningsystem is coupled to a digital data acquisition system where the outputof the absolute positioning system will have a finite resolution andsampling frequency. The absolute positioning system may comprise acompare-and-combine circuit to combine a computed response with ameasured response using algorithms, such as a weighted average and atheoretical control loop, that drive the computed response towards themeasured response. The computed response of the physical system may takeinto account properties like mass, inertia, viscous friction, inductanceresistance, etc., to predict what the states and outputs of the physicalsystem will be by knowing the input.

The absolute positioning system may provide an absolute position of thedisplacement member upon power-up of the instrument, without retractingor advancing the displacement member to a reset (zero or home) positionas may be required with conventional rotary encoders that merely countthe number of steps forwards or backwards that the motor 20230 has takento infer the position of a device actuator, drive bar, knife, or thelike.

A sensor 20226, such as, for example, a strain gauge or a micro-straingauge, may be configured to measure one or more parameters of the endeffector, such as, for example, the amplitude of the strain exerted onthe anvil during a clamping operation, which can be indicative of theclosure forces applied to the anvil. The measured strain may beconverted to a digital signal and provided to the processor 20222.Alternatively, or in addition to the sensor 20226, a sensor 20227, suchas, for example, a load sensor, can measure the closure force applied bythe closure drive system to the anvil. The sensor 20227, such as, forexample, a load sensor, can measure the firing force applied to anI-beam in a firing stroke of the surgical instrument or tool. The I-beamis configured to engage a wedge sled, which is configured to upwardlycam staple drivers to force out staples into deforming contact with ananvil. The I-beam also may include a sharpened cutting edge that can beused to sever tissue as the I-beam is advanced distally by the firingbar. Alternatively, a current sensor 20231 can be employed to measurethe current drawn by the motor 20230. The force required to advance thefiring member can correspond to the current drawn by the motor 20230,for example. The measured force map be converted to a digital signal andprovided to the processor 20222.

In one form, the strain gauge sensor 20226 can be used to measure theforce applied to the tissue by the end effector. A strain gauge can becoupled to the end effector to measure the force on the tissue beingtreated by the end effector. A system for measuring forces applied tothe tissue grasped by the end effector may comprise a strain gaugesensor 20226, such as, for example, a micro-strain gauge, that can beconfigured to measure one or more parameters of the end effector, forexample. In one aspect, the strain gauge sensor 20226 can measure theamplitude or magnitude of the strain exerted on a jaw member of an endeffector during a clamping operation, which can be indicative of thetissue compression. The measured strain can be converted to a digitalsignal and provided to a processor 20222 of the microcontroller 20221. Aload sensor 20227 can measure the force used to operate the knifeelement, for example, to cut the tissue captured between the anvil andthe staple cartridge. A magnetic field sensor can be employed to measurethe thickness of the captured tissue. The measurement of the magneticfield sensor also may be converted to a digital signal and provided tothe processor 20222.

The measurements of the tissue compression, the tissue thickness, and/orthe force required to close the end effector on the tissue, asrespectively measured by the sensors 20226, 20227, can be used by themicrocontroller 20221 to characterize the selected position of thefiring member and/or the corresponding value of the speed of the firingmember. In one instance, a memory 20223 may store a technique, anequation, and/or a lookup table which can be employed by themicrocontroller 20221 in the assessment.

The control system 20220 of the surgical instrument or tool also maycomprise wired or wireless communication circuits to communicate withthe modular communication hub 20065 as shown in FIG. 5 and FIG. 6A.

FIG. 7B shows an example sensing system 20069. The sensing system may bea surgeon sensing system or a patient sensing system. The sensing system20069 may include a sensor unit 20235 and a human interface system 20242that are in communication with a data processing and communication unit20236. The data processing and communication unit 20236 may include ananalog-to-digital converted 20237, a data processing unit 20238, astorage unit 20239, and an input/output interface 20241, a transceiver20240. The sensing system 20069 may be in communication with a surgicalhub or a computing device 20243, which in turn is in communication witha cloud computing system 20244. The cloud computing system 20244 mayinclude a cloud storage system 20078 and one or more cloud servers20077.

The sensor unit 20235 may include one or more ex vivo or in vivo sensorsfor measuring one or more biomarkers. The biomarkers may include, forexample, Blood pH, hydration state, oxygen saturation, core bodytemperature, heart rate, Heart rate variability, Sweat rate, Skinconductance, Blood pressure, Light exposure, Environmental temperature,Respiratory rate, Coughing and sneezing, Gastrointestinal motility,Gastrointestinal tract imaging, Tissue perfusion pressure, Bacteria inrespiratory tract, Alcohol consumption, Lactate (sweat), Peripheraltemperature, Positivity and optimism, Adrenaline (sweat), Cortisol(sweat), Edema, Mycotoxins, VO2 max, Pre-operative pain, chemicals inthe air, Circulating tumor cells, Stress and anxiety, Confusion anddelirium, Physical activity, Autonomic tone, Circadian rhythm, Menstrualcycle, Sleep, etc. These biomarkers may be measured using one or moresensors, for example, photosensors (e.g., photodiodes, photoresistors),mechanical sensors (e.g., motion sensors), acoustic sensors, electricalsensors, electrochemical sensors, thermoelectric sensors, infraredsensors, etc. The sensors may measure the biomarkers as described hereinusing one of more of the following sensing technologies:photoplethysmography, electrocardiography, electroencephalography,colorimetry, impedimentary, potentiometry, amperometry, etc.

As illustrated in FIG. 7B, a sensor in the sensor unit 20235 may measurea physiological signal (e.g., a voltage, a current, a PPG signal, etc.)associated with a biomarker to be measured. The physiological signal tobe measured may depend on the sensing technology used, as describedherein. The sensor unit 20235 of the sensing system 20069 may be incommunication with the data processing and communication unit 20236. Inan example, the sensor unit 20235 may communicate with the dataprocessing and communication unit 20236 using a wireless interface. Thedata processing and communication unit 20236 may include ananalog-to-digital converter (ADC) 20237, a data processing unit 20238, astorage 20239, an I/O interface 20241, and an RF transceiver 20240. Thedata processing unit 20238 may include a processor and a memory unit.

The sensor unit 20235 may transmit the measured physiological signal tothe ADC 20237 of the data processing and communication unit 20236. In anexample, the measured physiological signal may be passed through one ormore filters (e.g., an RC low-pass filter) before being sent to the ADC.The ADC may convert the measured physiological signal into measurementdata associated with the biomarker. The ADC may pass measurement data tothe data processing unit 20238 for processing. In an example, the dataprocessing unit 20238 may send the measurement data associated with thebiomarker to a surgical hub or a computing device 20243, which in turnmay send the measurement data to a cloud computing system 20244 forfurther processing. The data processing unit may send the measurementdata to the surgical hub or the computing device 20243 using one of thewireless protocols, as described herein. In an example, the dataprocessing unit 20238 may first process the raw measurement datareceived from the sensor unit and send the processed measurement data tothe surgical hub or a computing device 20243.

In an example, the data processing and communication unit 20236 of thesensing system 20069 may receive a threshold value associated with abiomarker for monitoring from a surgical hub, a computing device 20243,or directly from a cloud server 20077 of the cloud computing system20244. The data processing unit 20236 may compare the measurement dataassociated with the biomarker to be monitored with the correspondingthreshold value received from the surgical hub, the computing device20243, or the cloud server 20077. The data processing and communicationunit 20236 may send a notification message to the HID 20242 indicatingthat a measurement data value has crossed the threshold value. Thenotification message may include the measurement data associated withthe monitored biomarker. The data processing and computing unit 20236may send a notification via a transmission to a surgical hub or acomputing device 20243 using one of the following RF protocols:Bluetooth, Bluetooth Low-Energy (BLE), Bluetooth Smart, Zigbee, Z-wave,IPv6 Low-power wireless Personal Area Network (6LoWPAN), Wi-Fi. The dataprocessing unit 20238 may send a notification (e.g., a notification foran HCP) directly to a cloud server via a transmission to a cellulartransmission/reception point (TRP) or a base station using one or moreof the following cellular protocols: GSM/GPRS/EDGE (2G), UMTS/HSPA (3G),long term evolution (LTE) or 4G, LTE-Advanced (LTE-A), new radio (NR) or5G. In an example, the sensing unit may be in communication with thehub/computing device via a router, as described in FIG. 6A through FIG.6C.

FIG. 7C shows an example sensing system 20069 (e.g., a surgeon sensingsystem or a patient sensing system). The sensing system 20069 mayinclude a sensor unit 20245, a data processing and communication unit20246, and a human interface device 20242. The sensor unit 20245 mayinclude a sensor 20247 and an analog-to-digital converted (ADC) 20248.The ADC 20248 in the sensor unit 20245 may convert a physiologicalsignal measured by the sensor 20247 into measurement data associatedwith a biomarker. The sensor unit 20245 may send the measurement data tothe data processing and communication unit 20246 for further processing.In an example, the sensor unit 20245 may send the measurement data tothe data processing and communication unit 20246 using aninter-integrated circuit (I2C) interface.

The data processing and communication unit 20246 includes a dataprocessing unit 20249, a storage unit 20250, and an RE transceiver20251. The sensing system may be in communication with a surgical hub ora computing device 20243, which in turn may be in communication with acloud computing system 20244. The cloud computing system 20244 mayinclude a remote server 20077 and an associated remote storage 20078.The sensor unit 20245 may include one or more ex vivo or in vivo sensorsfor measuring one or more biomarkers, as described herein.

The data processing and communication unit 20246 after processing themeasurement data received from the sensor unit 20245 may further processthe measurement data and/or send the measurement data to the smart hubor the computing device 20243, as described in FIG. 7B. In an example,the data processing and communication unit 20246 may send themeasurement data received front the sensor unit 20245 to the remoteserver 20077 of the cloud computing system 20244 for further processingand/or monitoring.

FIG. 7D shows an example sensing system 20069 (e.g., a surgeon sensingsystem or a patient sensing system). The sensing system 20069 mayinclude a sensor unit 20252, a data processing and communication unit20253, and a human interface system 20261. The sensor unit 20252 mayinclude a plurality of sensors 20254, 20255 up to 20256 to measure oneor more physiological signals associated with a patient or surgeonsbiomarkers and/or one or more physical state signals associated withphysical state of a patient or a surgeon. The sensor unit 20252 may alsoinclude one or more analog to-digital converter(s) (ADCs) 20257. A listof biomarkers may include biomarkers such as those biomarkers disclosedherein. The ADC(s) 20257 in the sensor unit 20252 may convert each ofthe physiological signals and/or physical state signals measured by thesensors 20254-20256 into respective measurement data. The sensor unit20252 may send the measurement data associated with one or morebiomarkers as well as with the physical state of a patient or a surgeonto the data processing and communication unit 20253 for furtherprocessing. The sensor unit 20252 may send the measurement data to thedata processing and communication unit 20253 individually for each ofthe sensors Sensor 1 20254 to Sensor N 20256 or combined for all thesensors. In an example, the sensor unit 20252 may send the measurementdata to the data processing and communication unit 20253 via an I2Cinterface.

The data processing and communication unit 20253 may include a dataprocessing unit 20258, a storage unit 20259, and an RF transceiver20260. The sensing system 20069 may be in communication with a surgicalhub or a computing device 20243, which in turn is in communication witha cloud computing system 20244 comprising at least one remote server20077 and at least one storage unit 20078. The sensor units 20252 mayinclude one or more ex vivo or in vivo sensors for measuring one or morebiomarkers, as described herein.

FIG. 8 is an example, of using a surgical task situational awareness andmeasurement data from one or more surgeon sensing systems to adjustsurgical instrument controls. FIG. 8 illustrates a timeline 20265 of anillustrative surgical procedure and the contextual information that asurgical hub can derive from data received from one or more surgicaldevices, one or more surgeon sensing systems, and/or one or moreenvironmental sensing systems at each step in the surgical procedure.The devices that could be controlled by a surgical hub may includeadvanced energy devices, endocutter clamps, etc. The surgeon sensingsystems may include sensing systems for measuring one or more biomarkersassociated with the surgeon, for example, heart rate, sweat composition,respiratory rate, etc. The environmental sensing system may includesystems for measuring one or more of the environmental attributes, forexample, cameras for detecting a surgeon's position/movements/breathingpattern, spatial microphones, for example to measure ambient noise inthe surgical theater and/or the tone of voice of a healthcare provider,temperature/humidity of the surroundings, etc.

In the following description of the timeline 20265 illustrated in FIG.8, reference should also be made to FIG. 5. FIG. 5 provides variouscomponents used in a surgical procedure. The timeline 20265 depicts thesteps that may be taken individually and/or collectively by the nurses,surgeons, and other medical personnel during the course of an exemplarycolorectal surgical procedure. In a colorectal surgical procedure, asituationally aware surgical hub 20076 may receive data from variousdata, sources throughout the course of the surgical procedure, includingdata generated each time a healthcare provider (HCP) utilizes a modulardevice/instrument 20095 that is paired with the surgical hub 20076. Thesurgical hub 20076 may receive this data from the paired modular devices20095. The surgical hub may receive measurement data from sensingsystems 20069. The surgical hub may use the data from the modulardevice/instruments 20095 and/or measurement data from the sensingsystems 20069 to continually derive inferences (i.e., contextualinformation) about an HCP's stress level and the ongoing procedure asnew data is received, such that the stress level of the surgeon relativeto the step of the procedure that is being performed is obtained. Thesituational awareness system of the surgical hub 20076 may perform oneor more of the following: record data pertaining to the procedure forgenerating reports, verify the steps being taken by the medicalpersonnel, provide data or prompts (e.g., via a display screen) that maybe pertinent for the particular procedural step, adjust modular devicesbased on the context (e.g., activate monitors, adjust the FOV of themedical imaging device, change the energy level of an ultrasonicsurgical instrument or RF electrosurgical instrument), or take any othersuch action described herein. In an example, these steps may beperformed by a remote server 20077 of a cloud system 20064 andcommunicated with the surgical hub 20076.

As a first step (not shown in FIG. 8 for brevity), the hospital staffmembers may retrieve the patient's EMR from the hospital's EMR database.Based on select patient data in the EMR, the surgical hub 20076 maydetermine that the procedure to be performed is a colorectal procedure.The staff members may scan the incoming medical supplies for theprocedure. The surgical hub 20076 may cross-reference the scannedsupplies with a list of supplies that can be utilized in various typesof procedures and confirms that the mix of supplies corresponds to acolorectal procedure. The surgical hub 20076 may pair each of thesensing systems 20069 worn by different HCPs.

Once each of the devices is ready and pre-surgical preparation iscomplete, the surgical team may begin by making incisions and placetrocars. The surgical team may perform access and prep by dissectingadhesions, if any, and identifying inferior mesenteric artery (IMA)branches. The surgical hub 20076 can infer that the surgeon is in theprocess of dissecting adhesions, at least based on the data it mayreceive from the RF or ultrasonic generator indicating that an energyinstrument is being fired. The surgical hub 20076 may cross-referencethe received data with the retrieved steps of the surgical procedure todetermine that an energy instrument being fired at this point in theprocess (e.g., after the completion of the previously discussed steps ofthe procedure) corresponds to the dissection step.

After dissection, the HCP may proceed to the ligation step (e.g.,indicated by A1) of the procedure. As illustrated in FIG. 8, the HCP maybegin by ligating the IMA. The surgical hub 20076 may infer that thesurgeon is ligating arteries and veins because it may receive data fromthe advanced energy jaw device and/or the endocutter indicating that theinstrument is being fired. The surgical hub may also receive measurementdata from one of the HCP's sensing systems indicating higher stresslevel of the HCP (e.g., indicated by B1 mark on the time axis). Forexample, higher stress level may be indicated by change in the HCP'sheart rate from a base value. The surgical hub 20076, like the priorstep, may derive this inference by cross-referencing the receipt of datafrom the surgical stapling and cutting instrument with the retrievedsteps in the process (e.g., as indicated by A2 and A3). The surgical hub20076 may monitor the advance energy jaw trigger ratio and/or theendocutter clamp and firing speed during the high stress time periods.In an example, the surgical hub 20076 may send an assistance controlsignal to the advanced energy jaw device and/or the endocutter device tocontrol the device in operation. The surgical hub may send theassistance signal based on the stress level of the HCP that is operatingthe surgical device and/or situational awareness known to the surgicalhub. For example, the surgical hub 20076 may send control assistancesignals to an advanced energy device or an endocutter clamp, asindicated in FIG. 8 by A2 and A3.

The HCP may proceed to the next step of freeing the upper sigmoidfollowed by freeing descending colon, rectum, and sigmoid. The surgicalhub 20076 may continue to monitor the high stress markers of the HCP(e.g., as indicated by D1, E1 a, E1 b, F1). The surgical hub 20076 maysend assistance signals to the advanced energy jaw device and/or theendocutter device during the high stress tune periods, as illustrated inFIG. 8.

After mobilizing the colon, the HCP may proceed with the segmentectomyportion of the procedure. For example, the surgical hub 20076 may inferthat the HCP is transecting the bowel and sigmoid removal based on datafrom the surgical stapling and cutting instrument, including data fromits cartridge. The cartridge data can correspond to the size or type ofstaple being fired by the instrument, for example. As different types ofstaples are utilized for different types of tissues, the cartridge datacan thus indicate the type of tissue being stapled and/or transected. Itshould be noted that surgeons regularly switch back and forth betweensurgical stapling/cutting instruments and surgical energy (e.g., RF orultrasonic) instruments depending upon the step in the procedure becausedifferent instruments are better adapted for particular tasks.Therefore, the sequence in which the stapling/cutting instruments andsurgical energy instruments are used can indicate what step of theprocedure the surgeon is performing.

The surgical hub may determine and send a control signal to surgicaldevice based on the stress level of the HCP. For example, during timeperiod G1 b, a control signal G2 b may be sent to an endocutter clamp.Upon removal of the sigmoid, the incisions are closed, and thepost-operative portion of the procedure may begin. The patient'sanesthesia can be reversed. The surgical hub 20076 may infer that thepatient is emerging from the anesthesia based on one or more sensingsystems attached to the patient.

FIG. 9 is a block diagram of the computer-implemented interactivesurgical system with surgeon/patient monitoring, in accordance with atleast one aspect of the present disclosure. In one aspect, thecomputer-implemented interactive surgical system may be configured tomonitor surgeon biomarkers and/or patient biomarkers using one or moresensing systems 20069. The surgeon biomarkers and/or the patientbiomarkers may be measured before, after, and/or during a surgicalprocedure. In one aspect, the computer-implemented interactive surgicalsystem may be configured to monitor and analyze data related to theoperation of various surgical systems 20069 that include surgical hubs,surgical instruments, robotic devices and operating theaters orhealthcare facilities. The computer-implemented interactive surgicalsystem may include a cloud-based analytics system. The cloud-basedanalytics system may include one or more analytics servers.

As illustrated in FIG. 9, the cloud-based monitoring and analyticssystem may comprise a plurality of sensing systems 20268 (may be thesame or similar to the sensing systems 20069), surgical instruments20266 (may be the same or similar to instruments 20031), a plurality ofsurgical hubs 20270 (may be the same or similar to hubs 20006), and asurgical data network 20269 (may be the same or similar to the surgicaldata network described in FIG. 4) to couple the surgical hubs 20270 tothe cloud 20271 (may be the same or similar to cloud computing system20064). Each of the plurality of surgical hubs 20270 may becommunicatively coupled to one or more surgical instruments 20266. Eachof the plurality of surgical hubs 20270 may also be communicativelycoupled to the one or more sensing systems 20268, and the cloud 20271 ofthe computer-implemented interactive surgical system via the network20269. The surgical hubs 20270 and the sensing systems 20268 may becommunicatively coupled using wireless protocols as described herein.The cloud system 20271 may be a remote centralized source of hardwareand software for storing, processing, manipulating, and communicatingmeasurement data from the sensing systems 20268 and data generated basedon the operation of various surgical systems 20268.

As shown in FIG. 9, access to the cloud system 20271 may be achieved viathe network 20269, which may be the Internet or some other suitablecomputer network. Surgical hubs 20270 that may be coupled to the cloudsystem 20271 can be considered the client side of the cloud computingsystem (e.g., cloud-based analytics system). Surgical instruments 20266may be paired with the surgical hubs 20270 for control andimplementation of various surgical procedures and/or operations, asdescribed herein. Sensing systems 20268 may be paired with surgical hubs20270 for in-surgical surgeon monitoring of surgeon related biomarkers,pre-surgical patient monitoring, in-surgical patient monitoring, orpost-surgical monitoring of patient biomarkers to track and/or measurevarious milestones and/or detect various complications. Environmentalsensing systems 20267 may be paired with surgical hubs 20270 measuringenvironmental attributes associated with a surgeon or a patient forsurgeon monitoring, pre-surgical patient monitoring, in-surgical patientmonitoring, or post-surgical monitoring of patient.

Surgical instruments 20266, environmental sensing systems 20267, andsensing systems 20268 may comprise wired or wireless transceivers fordata transmission to and from their corresponding surgical hubs 20270(which may also comprise transceivers). Combinations of one or more ofsurgical instruments 20266, sensing systems 20268, or surgical hubs20270 may indicate particular locations, such as operating theaters,intensive care unit (ICU) rooms, or recovery rooms in healthcarefacilities (e.g., hospitals), for providing medical operations,pre-surgical preparation, and/or post-surgical recovery. For example,the memory of a surgical hub 20270 may store location data.

As shown in FIG. 9, the cloud system 20271 may include one or morecentral servers 20272 (may be same or similar to remote server 20067),surgical hub application servers 20276, data analytics modules 20277,and an input/output (“I/O”) interface 20278, The central servers 20272of the cloud system 20271 may collectively administer the cloudcomputing system, which includes monitoring requests by client surgicalhubs 20270 and managing the processing capacity of the cloud system20271 for executing the requests. Each of the central servers 20272 maycomprise one or more processors 20273 coupled to suitable memory devices20274 which can include volatile memory such as random-access memory(RAM) and non-volatile memory such as magnetic storage devices. Thememory devices 20274 may comprise machine executable instructions thatwhen executed cause the processors 20273 to execute the data analyticsmodules 20277 for the cloud-based data analysis, real-time monitoring ofmeasurement data received from the sensing systems 20268, operations,recommendations, and other operations as described herein. Theprocessors 20273 can execute the data analytics modules 20277independently or in conjunction with hub applications independentlyexecuted by the hubs 20270. The central servers 20272 also may compriseaggregated medical data databases 20275, which can reside in the memory20274.

Based on connections to various surgical hubs 20270 via the network20269, the cloud 20271 can aggregate data from specific data generatedby various surgical instruments 20266 and/or monitor real-time datafront sensing systems 20268 and the surgical hubs 20270 associated withthe surgical instruments 20266 and/or the sensing systems 20268. Suchaggregated data from the surgical instruments 20266 and/or measurementdata from the sensing systems 20268 may be stored within the aggregatedmedical databases 20275 of the cloud 20271. In particular, the cloud20271 may advantageously track real-time measurement data from thesensing systems 20268 and/or perform data analysis and operations on themeasurement data and/or the aggregated data to yield insights and/orperform functions that individual hubs 20270 could not achieve on theirown. To this end, as shown in FIG. 9, the cloud 20271 and the surgicalhubs 20270 are communicatively coupled to transmit and receiveinformation. The I/O interface 20278 is connected to the plurality ofsurgical hubs 20270 via the network 20269. In this way, the I/Ointerface 20278 can be configured to transfer information between thesurgical hubs 20270 and the aggregated medical data databases 20275.Accordingly, the I/O interface 20278 may facilitate read/writeoperations of the cloud-based analytics system. Such read/writeoperations may be executed in response to requests from hubs 20270.These requests could be transmitted to the surgical hubs 20270 throughthe hub applications. The T/0 interface 20278 may include one or morehigh speed data ports, which may include universal serial bus (USB)ports, IEEE 1394 ports, as well as Wi-Fi and Bluetooth I/O interfacesfor connecting the cloud 20271 to surgical hubs 20270. The hubapplication servers 20276 of the cloud 20271 may be configured to hostand supply shared capabilities to software applications (e.g., hubapplications) executed by surgical hubs 20270. For example, the hubapplication servers 20276 may manage requests made by the hubapplications through the hubs 20270, control access to the aggregatedmedical data databases 20275, and perform load balancing.

The cloud computing system configuration described in the presentdisclosure may be designed to address various issues arising in thecontext of medical operations (e.g., pre-surgical monitoring,in-surgical monitoring, and post-surgical monitoring) and proceduresperformed using medical devices, such as the surgical instruments 20266,20031. In particular, the surgical instruments 20266 may be digitalsurgical devices configured to interact with the cloud 20271 forimplementing techniques to improve the performance of surgicaloperations. The sensing systems 20268 may be systems with one or moresensors that are configured to measure one or more biomarkers associatedwith a surgeon perfuming a medical operation and/or a patient on whom amedical operation is planned to be performed, is being performed or hasbeen performed. Various surgical instruments 20266, sensing systems20268, and/or surgical hubs 20270 may include human interface systems(e.g., having a touch-controlled user interfaces) such that cliniciansand/or patients may control aspects of interaction between the surgicalinstruments 20266 or the sensing system 20268 and the cloud 20271. Othersuitable user interfaces for control such as auditory controlled userinterfaces may also be used.

The cloud computing system configuration described in the presentdisclosure may be designed to address various issues arising in thecontext of monitoring one or more biomarkers associated with ahealthcare professional (HCP) or a patient in pre-surgical, in-surgical,and post-surgical procedures using sensing systems 20268. Sensingsystems 20268 may be surgeon sensing systems or patient sensing systemsconfigured to interact with the surgical hub 20270 and/or with the cloudsystem 20271 for implementing, techniques to monitor surgeon biomarkersand/or patient biomarkers. Various sensing systems 20268 and/or surgicalhubs 20270 may comprise touch-controlled human interface systems suchthat the HCPs or the patients may control aspects of interaction betweenthe sensing systems 20268 and the surgical hub 20270 and/or the cloudsystems 20271. Other suitable user interfaces for control such asauditory controlled user interfaces may also be used.

FIG. 10 illustrates an example surgical system 20280 in accordance withthe present disclosure and may include a surgical instrument 20282 thatcan be in communication with a console 20294 or a portable device 20296through a local area network 20292 or a cloud network 20293 via a wiredor wireless connection. In various aspects, the console 20294 and theportable device 20296 may be any suitable computing device. The surgicalinstrument 20282 may include a handle 20297, an adapter 20285, and aloading unit 20287. The adapter 20285 releasably couples to the handle20297 and the loading unit 20287 releasably couples to the adapter 20285such that the adapter 20285 transmits a force from a drive shaft to theloading unit 20287. The adapter 20285 or the loading unit 20287 mayinclude a force gauge (not explicitly shown) disposed therein to measurea force exerted on the loading unit 20287. The loading unit 20287 mayinclude an end effector 20289 having a first jaw 20291 and a second jaw20290. The loading unit 20287 may be an in-situ loaded or multi-firingloading unit (MFLU) that allows a clinician to fire a plurality offasteners multiple times without requiring the loading unit 20287 to beremoved from a surgical site to reload the loading unit 20287.

The first and second jaws 20291, 20290 may be configured to clamp tissuetherebetween, fire fasteners through the clamped tissue, and sever theclamped tissue. The first jaw 20291 may be configured to fire at leastone fastener a plurality of times or may be configured to include areplaceable multi-fire fastener cartridge including a plurality offasteners (e.g., staples, clips, etc.) that may be fired more than onetime prior to being replaced. The second jaw 20290 may include an anvilthat deforms or otherwise secures the fasteners, as the fasteners areejected from the multi-fire fastener cartridge.

The handle 20297 may include a motor that is coupled to the drive shaftto affect rotation of the drive shaft. The handle 20297 may include acontrol interface to selectively activate the motor. The controlinterface may buttons, switches, levers, sliders, touchscreen, and anyother suitable input mechanisms or user interfaces, which can be engagedby a clinician to activate the motor.

The control interface of the handle 20297 may be in communication with acontroller 20298 of the handle 20297 to selectively activate the motorto affect rotation of the drive shafts. The controller 20298 may bedisposed within the handle 20297 and may be configured to receive inputfrom the control interface and adapter data from the adapter 20285 orloading unit data from the loading unit 20287. The controller 20298 mayanalyze the input from the control interface and the data received fromthe adapter 20285 and/or loading unit 20287 to selectively activate themotor. The handle 20297 may also include a display that is viewable by aclinician during use of the handle 20297. The display may be configuredto display portions of the adapter or loading unit data before, during,or after firing of the instrument 20282.

The adapter 20285 may include an adapter identification device 20284disposed therein and the loading unit 20287 may include a loading unitidentification device 20288 disposed therein. The adapter identificationdevice 20284 may be in communication with the controller 20298, and theloading unit identification device 20288 may be in communication withthe controller 20298. it will be appreciated that the loading unitidentification device 20288 may be in communication with the adapteridentification device 20284, which relays or passes communication frontthe loading unit identification device 20288 to the controller 20298.

The adapter 20285 may also include a plurality of sensors 20286 (oneshown) disposed thereabout to detect various conditions of the adapter20285 or of the environment (e.g., if the adapter 20285 is connected toa loading unit, if the adapter 20285 is connected to a handle, if thedrive shafts are rotating, the torque of the drive shafts, the strain ofthe drive shafts, the temperature within the adapter 20285, a number offirings of the adapter 20285, a peak force of the adapter 20285 duringfiring, a total amount of force applied to the adapter 20285, a peakretraction force of the adapter 20285, a number of pauses of the adapter20285 during firing, etc.). The plurality of sensors 20286 may providean input to the adapter identification device 20284 in the form of datasignals. The data signals of the plurality of sensors 20286 may bestored within or be used to update the adapter data stored within theadapter identification device 20284. The data signals of the pluralityof sensors 20286 may be analog or digital. The plurality of sensors20286 may include a force gauge to measure a force exerted on theloading unit 20287 during firing.

The handle 20297 and the adapter 20285 can be configured to interconnectthe adapter identification device 20284 and the loading unitidentification device 20288 with the controller 20298 via an electricalinterface. The electrical interface may be a direct electrical interface(i.e., include electrical contacts that engage one another to transmitenergy and signals therebetween). Additionally, or alternatively, theelectrical interface may be a non-contact electrical interface towirelessly transmit energy and signals therebetween (e.g., inductivelytransfer). It is also contemplated that the adapter identificationdevice 20284 and the controller 20298 may be in wireless communicationwith one another via a wireless connection separate from the electricalinterface.

The handle 20297 may include a transceiver 20283 that is configured totransmit instrument data from the controller 20298 to other componentsof the system 20280 (e.g., the LAN 20292, the cloud 20293, the console20294, or the portable device 20296). The controller 20298 may alsotransmit instrument data and/or measurement data associated with one ormore sensors 20286 to a surgical hub 20270, as illustrated in FIG. 9.The transceiver 20283 may receive data (e.g., cartridge data, loadingunit data, adapter data, or other notifications) from the surgical hub20270. The transceiver 20283 may receive data (e.g., cartridge data,loading unit data, or adapter data) from the other components of thesystem 20280. For example, the controller 20298 may transmit instrumentdata including a serial number of an attached adapter (e.g., adapter20285) attached to the handle 20297, a serial number of a loading unit(e.g., loading unit 20287) attached to the adapter 20285, and a serialnumber of a multi-fire fastener cartridge loaded into the loading unitto the console 20294. Thereafter, the console 20294 may transmit data(e.g., cartridge data, loading unit data, or adapter data) associatedwith the attached cartridge, loading unit, and adapter, respectively,back to the controller 20298. The controller 20298 can display messageson the local instrument display or transmit the message, via transceiver20283, to the console 20294 (yr the portable device 20296 to display themessage on the display 20295 or portable device screen, respectively.

FIG. 11A to FIG. 11D illustrates examples of wearable sensing systems,e.g., surgeon sensing systems or patient sensing systems. FIG. 11A is anexample of eyeglasses-based sensing system 20300 that may be based on anelectrochemical sensing platform. The sensing system 20300 may becapable of monitoring (e.g., real-time monitoring) of sweat electrolytesand/or metabolites using multiple sensors 20304 and 20305 that are incontact with the surgeon's or patient's skin. For example, the sensingsystem 20300 may use an amperometry based biosensor 20304 and/or apotentiometry based biosensor 20305 integrated with the nose bridge padsof the eyeglasses 20302 to measure current and/or the voltage.

The amperometric biosensor 20304 may be used to measure sweat lactatelevels (e.g., in mmol/L). Lactate that is a product of lactic acidosisthat may occur due to decreased tissue oxygenation, which may be causedby sepsis or hemorrhage. A patient's lactate levels (e.g., >2 mmol/L)may be used to monitor the onset of sepsis, for example, duringpost-surgical monitoring. The potentiometric biosensor 20305 may be usedto measure potassium levels in the patient's sweat. A voltage followercircuit with an operational amplifier may be used for measuring thepotential signal between the reference and the working electrodes. Theoutput of the voltage follower circuit may be filtered and convertedinto a digital value using an ADC.

The amperometric sensor 20304 and the potentiometric sensor 20305 may beconnected to circuitries 20303 placed on each of the arms of theeyeglasses. The electrochemical sensors may be used for simultaneousreal-time monitoring if sweat lactate and potassium levels. Theelectrochemical sensors may be screen printed on stickers and placed oneach side of the glasses nose pads to monitor sweat metabolites andelectrolytes. The electronic circuitries 20303 placed on the arms of theglasses frame may include a wireless data transceiver (e.g., a lowenergy Bluetooth transceiver) that may be used to transmit the lactateand/or potassium measurement data to a surgical hub or an intermediarydevice that may then forward the measurement data to the surgical hub.The eyeglasses-based sensing system 20300 may use signal conditioningunit to filter and amplify the electrical signal generated from theelectrochemical sensors 20305 or 20304, a microcontroller to digitizethe analog signal, and a wireless (e.g., a low energy Bluetooth) moduleto transfer the data to a surgical hub or a computing device, forexample, as described in FIGS. 7B through 7D.

FIG. 11B is an example of a wristband-type sensing system 20310comprising a sensor assembly 20312 (e.g., Photoplethysmography(PPG)-based sensor assembly or Electrocardiogram (ECG) based-sensorassembly). For example, in the sensing system 20310, the sensor assembly20312 may collect and analyze arterial pulse in the wrist. The sensorassembly 20312 may be used to measure one or more biomarkers (e.g.,heart rate, heart rate variability (HRV), etc.). In case of a sensingsystem with a PPG-based sensor assembly 20312, light (e.g., green light)may be passed through the skin. A percentage of the green light may beabsorbed by the blood vessels and some of the green light may bereflected and detected by a photodetector. These differences orreflections are associated with the variations in the blood perfusion ofthe tissue and the variations may be used in detecting the heart-relatedinformation of the cardiovascular system (e.g., heart rate). Forexample, the amount of absorption may vary depending on the bloodvolume. The sensing system 20310 may determine the heart rate bymeasuring light reflectance as a function of time. HRV may be determinedas the time period variation (e.g., standard deviation) between thesteepest signal gradient prior to a peak, known as inter-beat intervals(IBIs).

In the case or a sensing system with an ECG-based sensor assembly 20312,a set or electrodes may be placed in contact with skin. The sensingsystem 20310 may measure voltages across the set of electrodes placed onthe skin to determine heart rate. HRV in this case may be measured asthe tune period variation (e.g., standard deviation) between R peaks inthe QRS complex, known as R-R intervals.

The sensing system 20310 may use a signal conditioning unit to filterand amplify the analog PPG signal, a microcontroller to digitize theanalog PPG signal, and a wireless (e.g., a Bluetooth) module to transferthe data to a surgical hub or a computing device, for example, asdescribed in FIGS. 7B through 7D.

FIG. 11C is an example ring sensing system 20320. The ring sensingsystem 20320 may include a sensor assembly (e.g., a heart rate sensorassembly) 20322. The sensor assembly 20322 may include a light source(e.g., red or green light emitting diodes (LEDs)), and photodiodes todetect reflected and/or absorbed light. The LEDs in the sensor assembly20322 may shine light through a finger and the photodiode in the sensorassembly 20322 may measure heart rate and/or oxygen level in the bloodby detecting blood volume change. The ring sensing system 20320 mayinclude other sensor assemblies to measure other biomarkers, forexample, a thermistor or an infrared thermometer to measure the surfacebody temperature. The ring sensing system 20320 may use a signalconditioning unit to filter and amplify the analog PPG signal, amicrocontroller to digitize the analog PPG signal, and a wireless (e.g.,a low energy Bluetooth) module to transfer the data to a surgical hub ora computing device, for example, as described in FIGS. 7B through 7D.

FIG. 11D is an example of an electroencephalogram (EEG) sensing system20315. As illustrated in FIG. 11D, the sensing system 20315 may includeone or more EEG sensor units 20317. The EEG sensor units 20317 mayinclude a plurality of conductive electrodes placed in contact with thescalp. The conductive electrodes may be used to measure small electricalpotentials that may arise outside of the head due to neuronal actionwithin the brain. The EEG sensing system 20315 may measure a biomarker,for example, delirium by identifying certain brain patterns, forexample, a slowing or dropout of the posterior dominant rhythm and lossof reactivity to eyes opening and closing. The ring sensing system 20315may have a signal conditioning unit for filtering and amplifying theelectrical potentials, a microcontroller to digitize the electricalsignals, and a wireless (e.g., a low energy Bluetooth) module totransfer the data to a smart device, for example, as described in FIGS.7B through 7D.

FIG. 12 illustrates a block diagram of a computer-implementedpatient/surgeon monitoring system 20325 for monitoring one or morepatient or surgeon biomarkers prior to, during, and/or after a surgicalprocedure, As illustrated in FIG. 12, one or more sensing systems 20336may be used to measure and monitor the patient biomarkers, for example,to facilitate patient preparedness before a surgical procedure, andrecovery after a surgical procedure. Sensing systems 20336 may be usedto measure and monitor the surgeon biomarkers in real-time, for example,to assist surgical tasks by communicating relevant biomarkers (e.g.,surgeon biomarkers) to a surgical hub 20326 and/or the surgical devices20337 to adjust their function. The surgical device functions that maybe adjusted may include power levels, advancement speeds, closure speed,loads, wait times, or other tissue dependent operational parameters. Thesensing systems 20336 may also measure one or more physical attributesassociated with a surgeon or a patient. The patient biomarkers and/orthe physical attributes may be measured in real time.

The computer-implemented wearable patient/surgeon wearable sensingsystem 20325 may include a surgical hub 20326, one or more sensingsystems 20336, and one or more surgical devices 20337. The sensingsystems and the surgical devices may be communicably coupled to thesurgical hub 20326. One or more analytics servers 20338, for examplepart of an analytics system, may also be communicably coupled to thesurgical hub 20326. Although a single surgical hub 20326 is depicted, itshould be noted that the wearable patient/surgeon wearable sensingsystem 20325 may include any number of surgical hubs 20326, which can beconnected to form a network of surgical hubs 20326 that are communicablycoupled to one or more analytics servers 20338, as described herein.

In an example, the surgical hub 20326 may be a computing device. Thecomputing device may be a personal computer, a laptop, a tablet, a smartmobile device, etc. In an example, the computing device may be a clientcomputing device of a cloud-based computing system. The client computingdevice may be a thin client.

In an example, the surgical hub 20326 may include a processor 20327coupled to a memory 20330 for executing instructions stored thereon, astorage 20331 to store one or more databases such as an EMR database,and a data relay interface 20329 through which data is transmitted tothe analytics servers 20338. In an example, the surgical hub 20326further may include an I/O interface 20333 having an input device 20341(e.g., a capacitive touchscreen or a keyboard) for receiving inputs froma user and an output device 20335 (e.g., a display screen) for providingoutputs to a user. In an example, the input device and the output devicemay be a single device. Outputs may include data from a query input bythe user, suggestions for products or a combination of products to usein a given procedure, and/or instructions for actions to be carried outbefore, during, and/or after a surgical procedure. The surgical hub20326 may include a device interface 20332 for communicably coupling thesurgical devices 20337 to the surgical hub 20326. In one aspect, thedevice interface 20332 may include a transceiver that may enable one ormore surgical devices 20337 to connect with the surgical hub 20326 via awired interface or a wireless interface using one of the wired orwireless communication protocols described herein. The surgical devices20337 may include, for example, powered staplers, energy devices ortheir generators, imaging systems, or other linked systems, for example,smoke evacuators, suction-irrigation devices, insufflation systems, etc.

In an example, the surgical hub 20326 may be communicably coupled to oneor more surgeon and/or patient sensing systems 20336. The sensingsystems 20336 may be used to measure and/or monitor, in real-time,various biomarkers associated with a surgeon performing a surgicalprocedure or a patient on whom a surgical procedure is being performed.A list of the patient/surgeon biomarkers measured by the sensing systems20336 is provided herein. In an example, the surgical hub 20326 may becommunicably coupled to an environmental sensing system 20334. Theenvironmental sensing systems 20334 may be used to measure and/ormonitor, in real-time, environmental attributes, for example,temperature/humidity in the surgical theater, surgeon movements, ambientnoise in the surgical theater caused by the surgeon's and/or thepatient's breathing pattern, etc.

When sensing systems 20336 and the surgical devices 20337 are connectedto the surgical hub 20326, the surgical hub 20326 may receivemeasurement data associated with one or more patient biomarkers,physical state associated with a patient, measurement data associatedwith surgeon biomarkers, and/or physical state associated with thesurgeon from the sensing systems 20336, for example, as illustrated inFIG. 7B through 7D. The surgical hub 20326 may associate the measurementdata, e.g., related to a surgeon, with other relevant pre-surgical dataand/or data from situational awareness system to generate controlsignals for controlling the surgical devices 20337, for example, asillustrated in FIG. 8.

In an example, the surgical hub 20326 may compare the measurement datafrom the sensing systems 20336 with one or more thresholds defined basedon baseline values, pre-surgical measurement data, and/or in surgicalmeasurement data. The surgical hub 20326 may compare the measurementdata from the sensing systems 20336 with one or more thresholds inreal-time. The surgical hub 20326 may generate a notification fordisplaying. The surgical hub 20326 may send the notification fordelivery to a human interface system for patient 20339 and/or the humaninterface system for a surgeon or an HCP 20340, for example, if themeasurement data crosses (e.g., is greater than or lower than) thedefined threshold value. The determination whether the notificationwould be sent to one or more of the to the human interface system forpatient 20339 and/or the human interface system for an HCP 2340 may bebased on a severity level associated with the notification. The surgicalhub 20326 may also generate a severity level associated with thenotification for displaying. The severity level generated may bedisplayed to the patient and/or the surgeon or the HCP. In an example,the patient biomarkers to be measured and/or monitored (e.g., measuredand/or monitored in real-time) may be associated with a surgicalprocedural step. For example, the biomarkers to be measured andmonitored for transection of veins and arteries step of a thoracicsurgical procedure may include blood pressure, tissue perfusionpressure, edema, arterial stiffness, collagen content, thickness ofconnective tissue, etc., whereas the biomarkers to be measured andmonitored for lymph node dissection step of the surgical procedure mayinclude monitoring blood pressure of the patient. In an example, dataregarding postoperative complications could be retrieved from an EMRdatabase in the storage 20331 and data regarding staple or incision lineleakages could be directly detected or inferred by a situationalawareness system. The surgical procedural outcome data can be inferredby a situational awareness system from data received from a variety ofdata sources, including the surgical devices 20337, the sensing systems20336, and the databases in the storage 20331 to which the surgical hub20326 is connected.

The surgical hub 20326 may transmit the measurement data and physicalstate data it received from the sensing systems 20336 and/or dataassociated with the surgical devices 20337 to analytics servers 20338for processing thereon. Each of the analytics servers 20338 may includea memory and a processor coupled to the memory that may executeinstructions stored thereon to analyze the received data. The analyticsservers 20338 may be connected in a distributed computing architectureand/or utilize a cloud computing architecture. Based on this paireddata, the analytics system 20338 may determine optimal and/or preferredoperating parameters for the various types of modular devices, generateadjustments to the control programs for the surgical devices 20337, andtransmit (or “push”) the updates or control programs to the one or moresurgical devices 20337. For example, an analytics system 20338 maycorrelate the perioperative data it received from the surgical hub 20236with the measurement data associated with a physiological state of asurgeon or an HCP and/or a physiological state of the patient. Theanalytics system 20338 may determine when the surgical devices 20337should be controlled and send an update to the surgical hub 20326. Thesurgical hub 20326 may then forward the control program to the relevantsurgical device 20337.

Additional detail regarding the computer-implemented wearablepatient/surgeon wearable sensing system 20325, including the surgicalhub 30326, one or more sensing systems 20336 and various surgicaldevices 20337 connectable thereto, are described in connection with FIG.5 through FIG. 7D.

FIG. 13 shows an example of a computer implemented patient and surgeonmonitoring system 27200 that aggregates biomarker data. The aggregationof biomarker data provides insights to the facility of care, compliance,follow-up metric, and intervention accuracies. The computer-implementedpatient and surgeon monitoring system 27200 may include patient:monitored biomarkers 27202, facility hub datasets 27204, a computingsystem 27203, and a facility analytics system 27228.

The patient monitored biomarkers 27202 may be used to measure actualpatient biomarker data 27213. The patient monitored biomarkers 27202 mayinclude one or more of e following: Blood pH, hydration state, oxygensaturation, core body temperature, heart rate, Heart rate variability,Sweat rate, Skin conductance, Blood pressure, Light exposure,Environmental temperature, Respiratory rate, Coughing and sneezing,Gastrointestinal motility, Gastrointestinal tract, imaging, Tissueperfusion pressure, Bacteria in respiratory tract, Alcohol consumption,Lactate (sweat), Peripheral temperature, Positivity and optimism,Adrenaline (sweat), Cortisol (sweat), Edema, Mycotoxins, VO2 max,Pre-operative pain, chemicals in the air, Circulating tumor cells,Stress and anxiety, Confusion and delirium, Physical activity, Autonomictone, Circadian rhythm, Menstrual cycle, Sleep, etc. The patientmonitored biomarkers 27202 may be measured by controlled patient sensorsystems 27206 and uncontrolled patient sensor systems 27208. Thecontrolled patient sensor systems 27206 may measure the patientmonitored biomarkers 27202 in a controlled environment in closeproximity to an HCP (e.g., in a hospital recovery room). Theuncontrolled patient sensor systems 27208 may measure a patient in anuncontrolled environment not in close proximity to an HCP (e.g., apatient's residence). Each of the controlled patient sensor systems27206 and uncontrolled patient sensor systems 27208 may be measuredusing one or more sensors, for example, photosensors (e.g., photodiodes,photoresistors), mechanical sensors (e.g., motion sensors), acousticsensors, electrical sensors, electrochemical sensors, thermoelectricsensors, infrared sensors, etc. The sensors may measure the patientmonitored biomarkers 27202 as described herein using one of more of thefollowing sensing technologies: photoplethysmography,electrocardiography, electroeneephalography, colorimetry, impedimentary,potentiometry, amperometry, etc.

The facility hub datasets 27204 may provide information that is inputtedinto the computing system 27203 to compute expected patient biomarkerdata 27223. Facility hub datasets 27204 may include information from EMRdatabases 27214, treatment databases 27216, and HCP input 27218. EMRdatabases 27214 may include EMR information from specific patients fromspecific healthcare facilities. The EMR information may address certainissues unique to specific patients or specific groups of patients, suchas patients with diabetes, patients with cancer, patients with a historyof smoking, ext. Treatment databases 27216 may include treatmentinformation related to specific treatments being performed. Thetreatment information may address certain issues unique to each specifictreatment, such as issues related to heart surgery, issues related tobariatric surgery, and issues related to orthopedic procedures, ext. HCPinput 27218 may allow for an HCP to input any changes to any specificpatients and/or any specific procedures. For example, new developmentsin certain procedures and/or in a certain group of patients may impactthe expected patient biomarker data 27223 that may not necessarily bereflected in the previous data stored in the EMR databases 27214 and/orin the treatment databases 27216. The HCP input 27218 may allow for theHCP to input any changes necessary into the computing system 27203.

At 27210, the computing system 27203 may aggregate and filter thepatient monitored biomarkers 27202 measured by the controlled patientsensor systems 27206 and the uncontrolled patient sensor systems 27208to compute the actual patient biomarker data 27213. For example, thecomputing system 27203 may aggregate and filter the measured heart ratesof all patients on a recovery timeline after undergoing heart surgery.In examples, the data can be further aggregated and filtered by heartsurgeries performed by certain HCPs. In examples, the data can befurther aggregated and filtered by heart surgeries performed on patientswith diabetes, patients with a history of smoking, and/or patients thathave had multiple surgeries, ext. At 27212, the computing 27203 mayperform a pre-processing transform before outputting the actual patientbiomarker data 27213. For example, the pre-processing transform may useGaussian process regression to make predictions based on similar patientdata. The actual patient biomarker data 27213 may be stored in afacility notification database 27226.

At 27220, the computing system 27203 may aggregate and filter theinformation provided by the facility hub datasets 27204 to compute theexpected patient biomarker data 27223. For example, the computing systemmay aggregate and filter the heart rates of all patients previously on arecovery timeline after undergoing heart surgery. The heart rateinformation may be provided by the EMR databases 27214 and/or treatmentdatabases 27216. in examples, the data can be further aggregated andfiltered by heart surgeries previously performed by certain HCPs, whichmay also be provided by the EMR databases 27214 and/or treatmentdatabases 27216. In examples, the data can be further aggregated andfiltered by heart surgeries previously performed on patients withdiabetes, patients with a history of smoking, and/or patients that havehad multiple surgeries, ext., which may also be provided by the EMRdatabases 27214 and/or treatment databases 27216. The data aggregatedcan be used to predict the expected patient biomarker data 27223 andcompare it with the actual biomarker data 27213. At 27222, the computingsystem 27203 may perform a pre-processing transform before outputtingthe expected patient biomarker data 27213. For example, thepre-processing transform may use Gaussian process regression to makepredictions based on similar patient data. The expected patientbiomarker data 27223 may be stored in the facility notification database27226.

At 27224, the computing system 27203 may compute the differences betweenthe aggregated actual patient biomarker data 27213 and the aggregatedexpected patient biomarker data 27223. The differences may be stored inthe facility notification database 27226. In examples, the facilitynotification database 27226 may output alerts 27230 to HCPs, healthcarefacilities, and/or hospitals if the differences between the actualpatient biomarker data 27213 and the expected patient biomarker data27223 are over certain thresholds. In examples, the facilitynotification database 27226 may output alerts 27230 to HCPs, healthcarefacilities, and/or hospitals if the differences between the actualpatient biomarker data 27213 and the expected patient biomarker data27223 are under a certain threshold and/or are negligible. In examples,the facility notification database 27226 may output alerts 27230 of thedifferences between the actual patient biomarker data 27213 and theexpected patient biomarker data 27223 to HCPs, healthcare facilities,and/or hospitals at certain times, such as on a weekly, monthly, orsemi-monthly basis for monitoring. In examples, the facilitynotification database 27226 may output the differences to a facilityanalytics system 27228. The facility analytics system 27228 may includea facility analytics server that may perform analytics regarding thedata received. An HCP may view the analytics provided by the facilityanalytics system 27228 on a computing device to evaluate the careprovided to the user and when used with outcomes data can be used tomonitor trends of the facility and its HCPs. The data may be used toassess post-operative care, therapy compliance, and efficacy care fromwhich the provider can update care path based on monitored data. Thefacility analytics system 27228 can perform the data analytics in realtime. The facility analytics system 27228 is described further below inFIG. 16.

FIGS. 14A-14C show an example 27300 of the computer-implemented patientand surgeon monitoring system 27200 monitoring heart rate data of agroup of patients. In FIG. 14A, the facility hub datasets 27204 mayinclude information from the EMR databases 27214, the treatmentdatabases 27216, and HCP input 27218 to send to the computing device27203. As described in FIG. 13, the information from the facility hubdatasets 27204 may be aggregated and filtered at 27220 by the computingsystem 27203 to compute expected patient biomarker data 27223. As shownin FIG. 14A, the expected patient biomarker data may be an expectedheart rate 27302. In examples, the data from the facility hub datasets27204 can be further aggregated and filtered by heart surgeriesperformed by certain HCPs and/or certain health care facilities. Inexamples, the data can be further aggregated and filtered by heartsurgeries performed on patients with diabetes, patients with a historyof smoking, and/or patients that have bad multiple surgeries, ext. Theexpected heart rate 27302 may be stored in the facility notificationdatabase 27226. As shown in FIG. 14A, the expected heart rate 27302 maybe 60 beats/min, for example. The expected heart rate 27302 canrepresent the expected heart rate for a specific group of patients, suchas patients with diabetes, patients with a history of smoking, and/orpatients that have had multiple surgeries, ext. The expected heart rate27302 can represent the heart rates of patients over a recovery timelinefor surgeries performed by certain HCPs or certain healthcarefacilities, which can be used for monitoring HCPs and healthcarefacilities, as described further below in FIG. 16.

In FIG. 14B, the patient monitored biomarkers 27202 may be patient heartrates. The patient heart rates may be measured by controlled patientsensor systems 27206 and uncontrolled patient sensor systems 27208. Thecontrolled patient sensor systems 27206 may measure the patient heartrates in a controlled environment in close proximity to an HCP (e.g., ina hospital recovery room). The uncontrolled patient sensor systems 27208may measure patient heart rates in an uncontrolled environment not inclose proximity to an HCP (e.g., a patient's residence). As described inFIG. 13, the measurements from the controlled patient sensor systems27206 and uncontrolled patient sensor systems 27208 may be aggregatedand filtered at 27220 by the computing system 27203 to compute theactual heart rates 27304. For example, the computing system 27203 mayaggregate and filter the measured heart rates of all patients on arecovery timeline after undergoing heart surgery. In examples, the datacan be further aggregated and filtered by heart surgeries performed bycertain HCPs. In examples, the data can be further aggregated andfiltered by heart surgeries performed on patients with diabetes,patients with a history of smoking, and/or patients that have hadmultiple surgeries, ext. The actual heart rates 27304 may be stored in afacility notification database 27226. As shown in FIG. 14B, the actualheart rate 27304 may be 70 beats/min, for example. The actual heart rate27304 can represent the actual heart rate for a specific group ofpatients, such as patients with diabetes, patients with a history ofsmoking, and/or patients that have had multiple surgeries, ext. Theactual heart rate 27304 can represent the heart rates of patients over arecovery timeline for surgeries performed by certain HCPs or certainhealthcare facilities, which can be used for monitoring HCPs andhealthcare facilities, as described further below in FIG. 16.

In FIG. 14C, at 27224, the computing system 27203 may compute thedifferences between the aggregated actual heart rates 27304 and theaggregated expected heart rates 27302. The differences may be stored inthe facility notification database 27226. In examples, the facilitynotification database 27226 may output alerts 27306 to HCPs, healthcarefacilities, and/or hospitals if the differences between the actual heartrates 27304 and the expected heart rates 27302 are over certainthresholds. In examples, the facility notification database 27226 mayoutput alerts of the differences between the actual patient heart rates27304 and the expected patient heart rates 27302 to HCPs, healthcarefacilities, and/or hospitals at certain times, such as on a weekly,monthly, or semi-monthly basis for monitoring. The alerts 27306 can beviewed on computing device displays within healthcare facilities. Asshown in FIG. 14C, the alert 27306 may indicate the alert is regarding a“Deviation of Heart Rates” and show the deviation of the actual heartrate 27304 vs. the expected heart rate 27302, such as “+10 beats/min”,indicating the actual heart rate 27304 is 10 beats/min over the expectedheart rate 27302. As shown in FIG. 14C, the alert 27306 may also showthe exact numbers, and display “70 beats/min compared to expected 60beats/min”, for example. The alert 27306 may provide suggested actionsto take based on the deviations, such as to “monitor/check procedures”if the deviation is over a certain threshold amount, as shown in FIG.14C.

In examples, the patient biomarker data may be absolute and relativetemperature variations measured by temperature sensors. Measuringpatient body temperatures and body temperature variations may giveinsights into infection risk and allow HCPs to more discriminatelyprescribe antibiotics or different antibiotics. In examples, the patientbiomarker data may be orthopedic procedures such as knee replacement.The orthopedic procedures can be measured by sensors that can monitormotion (accelerometers) when possibly coupled with camera sensing, whichcould inform therapists of compliance of physical therapies. The rangeof motion data or other metrics could be computed to inform changes intherapy (increased repetitions, weights, etc.) to improve surgical care.

In various examples, the patient biomarker data may be heart variabilitycan be measured for meal detection, as described in U.S. Pat. No.8,696,616, titled OBESITY THERAPY AND HEART RATE VARIABILITY, filed Dec.29, 2010, the disclosure of which is herein incorporated by reference inits entirety. Meal detection may be used to detect the frequency andduration of meals following bariatric surgery. Meal detection may beused to determine the difference between eating and drinking. Complianceto post-operative meal detection protocols can be assessed and the dataused to provide feedback to patients. In other examples, alerts mayalert the HCP of trends showing potential problems or complications thatoccur without initial symptoms but can later be life threatening. Forexample, monitoring patient biomarkers such as hematocrit, bodytemperature, and/or heart rate signal changes, can show trendssuggestive of atrial-esophageal fistula after RF cardiac ablation, whichcan be life threatening after over 21 days without initial symptoms,allowing HCPs to take appropriate action preemptively to help savelives.

FIGS. 15A-15C show another example 27310 of the computer-implementedpatient and surgeon monitoring system 27200 monitoring heart rate dataof a group of patients. In FIG. 15A, like FIG. 14A, the facility hubdatasets 27204 may include information from the EMR databases 27214, thetreatment databases 27216, and HCP input 27218 to send to the computingdevice 27203. The expected heart rate 27312 can represent the expectedheart rate for a specific group of patients, such as patients withdiabetes, patients with a history of smoking, and/or patients that havehad multiple surgeries, ext. The expected heart rate 27312 may be storedin the facility notification database 27226. As shown in FIG. 15A, theexpected heart rate 27312 may be 60 beats/min, for example.

In FIG. 15B, like FIG. 14B, the patient monitored biomarkers 27202 maybe patient heart rates. The patient heart rates may be measured bycontrolled patient sensor systems 27206 and uncontrolled patient sensorsystems 27208. As described in FIG. 13, the measurements from thecontrolled patient sensor systems 27206 and uncontrolled patient sensorsystems 27208 may be aggregated and filtered at 27220 by the computingsystem 27203 to compute actual heart rates 27314. The actual heart rate27314 can represent the expected heart rate for a specific group ofpatients, such as patients with diabetes, patients with a history ofsmoking, and/or patients that have had multiple surgeries, ext. Theactual heat rate 27314 may be stored in a facility notification database27226. As shown in FIG. 15B, the actual heart 27314 rate may be 60beats/min, for example.

In FIG. 15C, at 27224, the computing system 27203 may compute thedifferences between the aggregated actual heart rates 27314 and theaggregated expected heart rates 27312. The differences may be stored inthe facility notification database 27226. In the example shown in FIG.15C, unlike in the example shown in FIG. 14C, the facility notificationdatabase 27226 may output alerts 27316 to HCPs, healthcare facilities,and/or hospitals if the differences between the actual heart rates 27314and the expected heart rates 27312 are within a desired range. Thealerts 27316 can be viewed on computing device displays withinhealthcare facilities. As shown in FIG. 15C, the alert 27316 mayindicate the alert is regarding a “Deviation of Heart Rates” and showthe deviation of the actual heart rate 27314 vs. the expected heart rate27312, such as “+0 beats/min”, indicating the actual heart rate 27314 isthe same as the expected heart rate 27312. As shown in FIG. 15C, thealert 27316 may also show the exact numbers, and display “60 beats/mincompared to expected 60 beats/min”, for example. The alert 27316 maydisplay a message to suggest the HCPs and/or the healthcare facilitiesare meeting certain outcome goals, such as “Excellent Work!” to provideencouragement, as shown in FIG. 15C.

FIG. 16 shows example facility analytics data 27320 that can be viewedon a computing device 27322 by an HCP. The facility analytics data 27320may be received from the facility analytics system 27228 within thecomputer-implemented patient and surgeon monitoring system 27200, asdescribed above in FIG. 13. The facility analytics system 27228 mayinclude a facility analytics server that may perform analytics regardingthe data received. The facility analytics system 27228 may perform thedata analytics in real time. For example, as shown in FIG. 16, thefacility analysis data 27320 may be represented by graph 27324 and graph27326. Also, shown in FIG. 16, the facility analytics data 27320 may begrouped based on certain procedures (represented by “Procedure Y” inFIG. 16) performed at certain health care facilities (represented by“Health Care Facility X” in FIG. 16). For example, graph 27324 may showthe deviation of heart rates across different patients throughout therecovery timeline after surgery. The deviation of heart rates may becomputed by the computing system 27203, which may compute thedifferences between the aggregated actual heart rates 27304, 27314 andthe aggregated expected heart rates 27302, 27312, as described above inFIGS. 14A-14C and FIGS. 15A-15C.

In examples, the data in graph 27324 may represent the deviation ofheart rates of all patients who underwent heart surgery at a certainhealth care facility and/or underwent heart surgery by a certain HCP. Inexamples, the data in graph 27324 may represent patients who underwentmultiple heart surgeries at a certain health care facility. In examples,the data in graph 27324 may represent patients with diabetes, patientswith a history of smoking, and/or patients with a history of irregularheartbeats. The data in graph 27324 may provide HCPs and/or health carefacilities with beneficial data, to monitor specific groups of patients.The data in graph 27324 may allow HCPs and/or health care facilities toanalyze patients having surgery outcomes that are meeting expectedoutcomes and patients having surgery outcomes that are not meetingexpected outcomes. The data in graph 27324 can also allow HCPs and/orhealthcare facilities to monitor how the deviations of heart rates arechanging throughout the recovery timeline after surgery for specificpatients. For example, the deviation of heart rates at three differentdiscrete times may be shown in bar graph format, which may be days,weeks, or months apart. If the recovery outcomes are not meetingexpectations throughout certain times during the recovery timeline,healthcare facilities can use this data to improve certain proceduresfor all patients and/or a specific group of patients. Additionally, ifcertain HCPs and/or healthcare facilities are not meeting expectationsthroughout certain nines during the recovery timeline, healthcarefacilities can use this data to improve protocols performed at certainfacilities or by certain HCPs by comparing protocols performed byhealthcare facilities and/or HCPs having less heart rate deviations andachieving more expected outcomes.

In examples, the data in graph 27326 may show the average deviation ofheart rates continuously for patients throughout a recovery timeline inline graph format. Graph 27326 may provide a quick, overall assessmentfor how certain health care facilities and/or HCPs are performingspecific procedures, such as heart surgeries. Like graph 27324, graph27326 may represent patients with diabetes, patients with a history ofsmoking, and/or patients with a history of irregular heartbeats. Likegraph 27324, graph 27326 may provide data to monitor certain HCPs and/orhealthcare facilities that are not meeting expectations throughout therecovery timeline. Healthcare facilities can use this data to improveprotocols performed at certain facilities or by certain HCPs bycomparing protocols performed by healthcare facilities and/or HCPshaving less heart rate deviations and achieving more expected outcomes.

FIG. 17 illustrates a process 27400 for a computing-implemented patientand surgeon monitoring system that aggregates biomarker data. Theprocess 27400 may be performed by the computing device 27203 aggregatingpatient biomarker data described above in FIG. 13. At 27402, thecomputing device 27203 may compute the expected patient biomarker data27223 for each of a plurality of patients. The expected patientbiomarker data 27223 represents expected values of patient biomarkersover the duration of the patient's recovery after undergoing a surgeryperformed by an HCP at a healthcare facility. The expected patientbiomarker data 27223 may be a set of values in a recovery timeline. At27404, the computing device 27203 may receive respective actual patientbiomarker data 27213 from respective patient sensor systems for each ofthe plurality of patients. The respective patient sensor systems may bethe controlled patient sensor systems 27206 and the uncontrolled patientsensor systems 27208 described above. At 27406, the computing device27203 may aggregate the respective expected patient biomarker data 27223and the respective actual patient biomarker data 27213 for each of theplurality of patients. The aggregated respective expected patientbiomarker data and the aggregated respective actual patient biomarkerdata may be by department or surgeon. The aggregated respective expectedpatient biomarker data and the aggregated respective actual patientbiomarker data may be by procedure configuration, surgical instrumentmix, complication type, re-admission rates, days of treatment, or timeto intervention. At 27408, the computing device may determine thedifferences between the aggregated respective expected patient biomarkerdata 27223 and the aggregated respective actual patient biomarker data27213 determined at 27406. The differences between the expected patientbiomarker data 27223 and the actual patient biomarker data 27213 at anygiven time may be for the same time in the patient's recovery. At 27410,the computing device may generate a treatment notification based on thedifferences determined at 27408. The treatment notification is a uniquenotification tailored for a specific group of patients. The treatmentnotification may provide insights to follow-up metrics, facility ofcare, compliance, and intervention accuracies.

FIG. 18 illustrates a process 27420 for a facility analytics system foranalyzing patient biomarker data. The process 27420 may be performed bythe facility analytics system 27320 analyzing patient biomarker datadescribed above in FIG. 16. At 27422, the facility analytics system27320 may establish communication with the computing device 27203. At27424, the facility analytics system 27320 may receive a treatmentnotification from the computing device 27203. The treatment notificationmay be based on the differences of aggregated respective expectedpatient biomarker data 27223 and aggregated respective actual patientbiomarker data for a plurality of patients. The expected patientbiomarker data 27223 may be a set of values in a recovery timeline. Thedifferences between the aggregated respective expected patient biomarkerdata 27223 and the aggregated respective actual patient biomarker data27213 at any given time may be for the same time in the patient'srecovery. The aggregated respective expected patient biomarker data27223 and the aggregated respective actual patient biomarker data 27213may be by department or surgeon. The aggregated respective expectedpatient biomarker data 27223 and the aggregated respective actualpatient biomarker data 27213 may be by procedure configuration, surgicalinstrument mix, complication type, re-admission rates, days oftreatment, or time to intervention. At 27426, the facility analyticssystem 27320 may perform analytics based on the treatment notificationreceived. The treatment notification may provide insights to follow-upmetrics, facility of care, compliance, and intervention accuracies.

The facility analytics system 27320 can provide many patient, HCP, andhealthcare facility benefits. In examples, the facility analytics can befor escalation of user interaction based on monitored biomarkers,patient interactive responses, and HCP response, timing, and comparativebest practices or facility improvement pre-set thresholds. In examples,the facility analytics can compare an expected event from an actualevent. In the case where the expected event is occurring but does notmatch the user's input, a prompt to conduct a further investigation canoccur. In examples, confirmation and verification of certain patientbiomarker data can be compared with other related patient biomarker datato confirm the reliability of the inputted patient biomarker data. Thepatient biomarker data could be vetted against reliability markers todetermine if the data is real or adjusted. The patient biomarker datacould be plotted to determine if a normal distribution occurs. Thepatient biomarker data could be compared to previous data points lookingfor exact measures. The system could occasionally ask for re-imputing ofthe last data point of the patient biomarker data without showing theuser the last data point they input for confirmation.

The facility analytics system 27320 can utilize the surgical datacollected by the computing device 27203 to help provide healthcareinputs to medical billing, medical record keeping, and medicationorders, for example. In examples, the facility analysis system 27320 cantransfer the facility analytics data to the billing system and submittedthe facility analytics data to insurance, Medicare, or other payers forreimbursement purposes. This could reduce resource required,inaccuracies and have documented evidence. The facility analytics system27320 can provide facility analytics data that contributes to a medicalrecords quality, such as improving physician documentation, lesseningthe need for handwriting legibility, and reducing duplication andinaccurate patient data. For example, if a clinical coder or HCP wasextracting data from a medical record in which the principal diagnoseswas unclear due to illegible handwriting, the health professional wouldhave to contact the physician responsible for documenting the diagnosesin order to correctly assign the code. In these examples, the facilityanalytics system 27320 eliminates the need for a HCP to contact thephysician responsible for documenting the diagnoses, and can simplyaccess the data on the computing device of the facility analytics system27320. The facility analytics system 27320 can provide medication ordersbased on the data received from the computing device 27203. The facilityanalytics system 27320 can offer a list of recommended medications thatcould be listed for the surgeon to select/approve, laboratory tests,imaging, and follow-up activities, based on task performed and thepatient biomarker data received.

We claim:
 1. A computer system for outcome tracking of a plurality ofpatients, comprising: a processor; and a memory coupled to theprocessor, the memory storing instructions, that when executed by theprocessor, cause the computer system to: generate a respective expectedpatient biomarker dataset for each of the plurality of patients, whereinthe expected patient biomarker dataset represents the expected values ofa patient biomarker over the duration of the patient's recovery; receiverespective actual patient biomarker data from respective patient sensorsystems for each of the plurality of patients; aggregate the respectiveexpected patient biomarker dataset and the respective actual patientbiomarker data for each of the plurality of patients; determinedifferences between the aggregated respective expected patient biomarkerdata and the aggregated respective actual patient biomarker data foreach of the plurality of patients; and generate a treatment notificationbased on the differences.
 2. The computer system of claim 1, wherein theaggregated differences between the respected expected patient biomarkerdata and the respective actual patient biomarker data at any given timeare for the same time in the patient's recovery.
 3. The computer systemof claim 1, wherein the expected patient biomarker data is a set ofvalues in a recovery timeline.
 4. The computer system of claim 1,wherein the treatment notification is a unique notification tailored fora specific group of patients.
 5. The computer system of claim 1, whereinthe aggregated respective expected patient biomarker data and theaggregated respective actual patient biomarker data is by department orsurgeon.
 6. The computer system of claim 1, wherein the aggregatedrespective expected patient biomarker data and the aggregated respectiveactual patient biomarker data is by procedure configuration, surgicalinstrument mix, complication type, re-admission rates, days oftreatment, or time to intervention.
 7. The computer system of claim 1,wherein the treatment notification provides insights to follow-upmetrics, facility of care, compliance, and intervention accuracies.
 8. Acomputer-implemented method for providing outcome tracking of aplurality of patients, comprising: generating a respective expectedpatient biomarker dataset for each of the plurality of patients, whereinthe expected patient biomarker dataset represents the expected values ofa patient biomarker over the duration of the patient's recovery;receiving respective actual patient biomarker data from respectivepatient sensor systems for each of the plurality of patients;aggregating the respective expected patient biomarker dataset and therespective actual patient biomarker data for each of the plurality ofpatients; determining differences between the respective expectedpatient biomarker data and the respective actual patient biomarker data;and generating a treatment notification based on the differences.
 9. Themethod of claim 8, wherein the differences between the aggregatedrespective expected patient biomarker data and the aggregated respectiveactual patient biomarker data at any given time are for the same time inthe patient's recovery.
 10. The method of claim 8, wherein the expectedpatient biomarker data is a set of values in a recovery timeline. 11.The method of claim 8, wherein the treatment notification is a uniquenotification tailored for a specific group of patients.
 12. The methodof claim 8, wherein the aggregated respective expected patient biomarkerdata and the aggregated respective actual patient biomarker data is bydepartment or surgeon.
 13. The method of claim 8, wherein the aggregatedrespective expected patient biomarker data and the aggregated respectiveactual patient biomarker data is by procedure configuration, surgicalinstrument mix, complication type, re-admission rates, days oftreatment, or time to intervention.
 14. The method of claim 8, whereinthe treatment notification provides insights to follow-up metrics,facility of care, compliance, and intervention accuracies.
 15. Afacility analytics system for outcome tracking of a plurality ofpatients, comprising: a processor; and a memory coupled to theprocessor, the memory storing instructions, that when executed by theprocessor, cause the facility analytics system to: establishcommunication with a computing device; receive a treatment notificationfrom the computing device, wherein the treatment notification is basedon differences of aggregated respective expected patient biomarker dataand aggregated respective actual patient biomarker data for a pluralityof patients; and perform facility analytics based on the treatmentnotification.
 16. The facility analytics system of claim 15, wherein thedifferences between the aggregated respective expected patient biomarkerdata and the aggregated respective actual patient biomarker data at anygiven time are for the same time in the patient's recovery.
 17. Thefacility analytics system of claim 15, wherein the expected patientbiomarker data is a set of values in a recovery timeline.
 18. Thefacility analytics system of claim 15, wherein the aggregated respectiveexpected patient biomarker data and the aggregated respective actualpatient biomarker data is by department or surgeon.
 19. The facilityanalytics system of claim 15, wherein the aggregated respective expectedpatient biomarker data and the aggregated respective actual patientbiomarker data is by procedure configuration, surgical instrument mix,complication type, re-admission rates, days of treatment, or time tointervention.
 20. The facility analytics system of claim 15, wherein thetreatment notification provides insights to follow-up metrics, facilityof care, compliance, and intervention accuracies.